{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 1: 肥胖风险类型预测\n",
    "\n",
    "## 数据描述\n",
    "\n",
    "在这个实验中，我们将使用深度学习来预测肥胖风险类型，数据集 `ORTP_2000.csv` 包含了 2000 个人的特征，我们将使用这些特征来预测个体的肥胖水平。以下是数据集中相关变量的描述：\n",
    "\n",
    "- Gender: 性别（男/女）  \n",
    "- Age: 年龄（岁）  \n",
    "- Height: 身高（米）  \n",
    "- Weight: 体重（千克）  \n",
    "- family_history_with_overweight: 是否有超重家族史（是/否）  \n",
    "- FAVC: 是否经常食用高热量食物（是/否）  \n",
    "- FCVC: 蔬菜摄入频率（1 到 3 级）  \n",
    "- NCP: 每日主餐次数  \n",
    "- CAEC: 进食零食的频率（有时、经常、总是、无）  \n",
    "- SMOKE: 是否吸烟（是/否）  \n",
    "- CH2O: 每日饮水量（升）  \n",
    "- SCC: 是否监测卡路里摄入（是/否）  \n",
    "- FAF: 体育活动频率（0 到 3 级）  \n",
    "- TUE: 使用电子设备的时间（0 到 2 级）  \n",
    "- CALC: 饮酒频率（有时、经常、总是、无）  \n",
    "- MTRANS: 主要出行方式（公共交通、汽车、步行等）  \n",
    "- NObeyesdad: 目标变量：肥胖分类（如正常体重、I 型肥胖、II 级超重等）  \n",
    "\n",
    "## 评估指标\n",
    "\n",
    "分类准确率，即分类正确的样本数占总样本数的比例：\n",
    "\n",
    "$$\n",
    "\\text{Accuracy} = \\frac{\\text{Number of correct predictions}}{\\text{Total number of predictions}}\n",
    "$$ \n",
    "\n",
    "## 要求\n",
    "\n",
    "50个epoch内，在训练集上的准确率大于90%, 在测试集上的准确率大于85%\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预处理\n",
    "\n",
    "我们先读取并观察数据的格式\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>FAVC</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>SMOKE</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>SCC</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "      <th>CALC</th>\n",
       "      <th>MTRANS</th>\n",
       "      <th>NObeyesdad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>24.443011</td>\n",
       "      <td>1.699998</td>\n",
       "      <td>81.669950</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.983297</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.763573</td>\n",
       "      <td>no</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.976473</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Overweight_Level_II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>no</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>no</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>no</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.711460</td>\n",
       "      <td>50.165754</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.880534</td>\n",
       "      <td>1.411685</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>1.910378</td>\n",
       "      <td>no</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>1.673584</td>\n",
       "      <td>no</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Insufficient_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>20.952737</td>\n",
       "      <td>1.710730</td>\n",
       "      <td>131.274851</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>1.674061</td>\n",
       "      <td>no</td>\n",
       "      <td>1.467863</td>\n",
       "      <td>0.780199</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Obesity_Type_III</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>31.641081</td>\n",
       "      <td>1.914186</td>\n",
       "      <td>93.798055</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.679664</td>\n",
       "      <td>1.971472</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>1.979848</td>\n",
       "      <td>no</td>\n",
       "      <td>1.967973</td>\n",
       "      <td>0.931721</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Overweight_Level_II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>Female</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>no</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>no</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>Female</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>no</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>Male</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.820000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>no</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>Male</td>\n",
       "      <td>24.825393</td>\n",
       "      <td>1.856759</td>\n",
       "      <td>120.980508</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.644094</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.155926</td>\n",
       "      <td>no</td>\n",
       "      <td>1.144076</td>\n",
       "      <td>0.673009</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Obesity_Type_II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>Male</td>\n",
       "      <td>25.314163</td>\n",
       "      <td>1.775580</td>\n",
       "      <td>112.256165</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.766612</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.116399</td>\n",
       "      <td>no</td>\n",
       "      <td>1.541072</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Obesity_Type_II</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender        Age    Height      Weight family_history_with_overweight  \\\n",
       "0       Male  24.443011  1.699998   81.669950                            yes   \n",
       "1     Female  18.000000  1.560000   57.000000                            yes   \n",
       "2     Female  18.000000  1.711460   50.165754                            yes   \n",
       "3     Female  20.952737  1.710730  131.274851                            yes   \n",
       "4       Male  31.641081  1.914186   93.798055                            yes   \n",
       "...      ...        ...       ...         ...                            ...   \n",
       "1995  Female  20.000000  1.560000   57.000000                            yes   \n",
       "1996  Female  22.000000  1.600000   58.000000                            yes   \n",
       "1997    Male  25.000000  1.820000   60.000000                            yes   \n",
       "1998    Male  24.825393  1.856759  120.980508                            yes   \n",
       "1999    Male  25.314163  1.775580  112.256165                            yes   \n",
       "\n",
       "     FAVC      FCVC       NCP        CAEC SMOKE      CH2O SCC       FAF  \\\n",
       "0     yes  2.000000  2.983297   Sometimes    no  2.763573  no  0.000000   \n",
       "1     yes  2.000000  3.000000  Frequently    no  2.000000  no  1.000000   \n",
       "2     yes  1.880534  1.411685   Sometimes    no  1.910378  no  0.866045   \n",
       "3     yes  3.000000  3.000000   Sometimes    no  1.674061  no  1.467863   \n",
       "4     yes  2.679664  1.971472   Sometimes    no  1.979848  no  1.967973   \n",
       "...   ...       ...       ...         ...   ...       ...  ..       ...   \n",
       "1995  yes  2.000000  4.000000  Frequently    no  2.000000  no  2.000000   \n",
       "1996   no  2.000000  3.000000   Sometimes    no  2.000000  no  2.000000   \n",
       "1997  yes  2.000000  3.000000   Sometimes    no  2.000000  no  2.000000   \n",
       "1998  yes  2.644094  3.000000   Sometimes    no  2.155926  no  1.144076   \n",
       "1999  yes  1.766612  3.000000   Sometimes    no  2.116399  no  1.541072   \n",
       "\n",
       "           TUE       CALC                 MTRANS           NObeyesdad  \n",
       "0     0.976473  Sometimes  Public_Transportation  Overweight_Level_II  \n",
       "1     1.000000         no             Automobile        Normal_Weight  \n",
       "2     1.673584         no  Public_Transportation  Insufficient_Weight  \n",
       "3     0.780199  Sometimes  Public_Transportation     Obesity_Type_III  \n",
       "4     0.931721  Sometimes  Public_Transportation  Overweight_Level_II  \n",
       "...        ...        ...                    ...                  ...  \n",
       "1995  0.000000  Sometimes  Public_Transportation        Normal_Weight  \n",
       "1996  0.000000  Sometimes             Automobile        Normal_Weight  \n",
       "1997  0.000000  Sometimes  Public_Transportation        Normal_Weight  \n",
       "1998  0.673009  Sometimes  Public_Transportation      Obesity_Type_II  \n",
       "1999  0.000000  Sometimes  Public_Transportation      Obesity_Type_II  \n",
       "\n",
       "[2000 rows x 17 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"ORTP_2000.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，我们的数据集中有 2000 个样本，每个样本有 16 个特征和 1 个标签（NObeyesdad）。\n",
    "\n",
    "其中特征有两种类型：\n",
    "\n",
    "- 一种是类别型，对于有序类别（如 \"低、中、高\"），常用Label Encoding编码；\n",
    "对于无序类别（如 \"Red, Blue, Green\"），常用One-Hot Encoding编码；\n",
    "- 一种是数值型，例如 Age、Height、Weight 等，我们将其归一化，使得均值为 0，方差为 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2000, 19), (2000,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder, OrdinalEncoder\n",
    "import numpy as np\n",
    "\n",
    "def preprocess_data(df):\n",
    "    \"\"\"\n",
    "    1. 对给定的 DataFrame 进行预处理，提取出 features 和 labels.\n",
    "    2. 将数值型的 features 进行标准化，将类别型的 features 和 labels 转换为相应编码\n",
    "\n",
    "    Args:\n",
    "        df (pandas.DataFrame): The input DataFrame containing the data.\n",
    "\n",
    "    Returns:\n",
    "        - features (numpy.Array): The preprocessed features.\n",
    "        - labels (numpy.Array): The labels.\n",
    "    \"\"\"\n",
    "    # 区分特征（无序类别、有序类别、数值型）和标签（有序类别）\n",
    "    nominal_features = [\"Gender\", \"family_history_with_overweight\", \"SMOKE\", \"SCC\", \"MTRANS\"]\n",
    "    ordinal_features = [\"FAVC\", \"CAEC\", \"CALC\"]\n",
    "    numerical_features = [\"Age\", \"Height\", \"Weight\", \"FCVC\", \"NCP\", \"CH2O\", \"FAF\", \"TUE\"]\n",
    "    label_column = \"NObeyesdad\"\n",
    "    \n",
    "    # 利用sklearn中的OrdinalEncoder对特征（有序类别）和标签（有序类别）编码\n",
    "    # 用OneHotEncoder对特征（无序类别）编码\n",
    "    # 用StandardScaler对特征（数值型）标准化\n",
    "    df[ordinal_features] = df[ordinal_features].apply(lambda x: x.str.capitalize())\n",
    "    ordinal_mappings = {\n",
    "        \"FAVC\": [[\"No\", \"Yes\"]],\n",
    "        \"CAEC\": [[\"No\", \"Sometimes\", \"Frequently\", \"Always\"]],\n",
    "        \"CALC\": [[\"No\", \"Sometimes\", \"Frequently\"]]\n",
    "    }\n",
    "    obesity_levels = [\"Insufficient_Weight\", \"Normal_Weight\", \"Overweight_Level_I\", \"Overweight_Level_II\", \n",
    "                    \"Obesity_Type_I\", \"Obesity_Type_II\", \"Obesity_Type_III\"]\n",
    "\n",
    "    ordinal_encoder = OrdinalEncoder(categories=[ordinal_mappings[feature][0] for feature in ordinal_features], handle_unknown='use_encoded_value', unknown_value=-1)\n",
    "    df[ordinal_features] = ordinal_encoder.fit_transform(df[ordinal_features])\n",
    "\n",
    "    encoder = OneHotEncoder(drop='first', sparse_output=False)\n",
    "    categorical_encoded = encoder.fit_transform(df[nominal_features])\n",
    "\n",
    "    label_encoder = OrdinalEncoder(categories=[obesity_levels])\n",
    "    df[label_column] = label_encoder.fit_transform(df[[label_column]])\n",
    "\n",
    "    scaler = StandardScaler()\n",
    "    numerical_scaled = scaler.fit_transform(df[numerical_features])\n",
    "\n",
    "    # 得到特征、标签（array形式）\n",
    "    features = np.hstack((numerical_scaled, categorical_encoded, df[ordinal_features].values))\n",
    "    labels = df[label_column].values.flatten()\n",
    "\n",
    "    return features, labels\n",
    "\n",
    "\n",
    "features, labels = preprocess_data(df)\n",
    "features.shape, labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据集的划分与加载\n",
    "\n",
    "我们按照80%，20%的比例将原数据集分为训练集和测试集。\n",
    "  \n",
    "为了保证实验结果的可复现性，我们设置随机数种子 seed=42。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset size: 1600\n",
      "Test dataset size: 400\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42, stratify=labels)\n",
    "print(f\"Train dataset size: {len(X_train)}\")\n",
    "print(f\"Test dataset size: {len(X_test)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们创建一个`ObesityDataset`类，继承自`torch.utils.data.Dataset`，这样方面后续处理与加载。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class ObesityDataset(Dataset):\n",
    "    \"\"\"\n",
    "    A custom dataset class for handling obesity data.\n",
    "\n",
    "    Args:\n",
    "        features (numpy.Array): The input features.\n",
    "        labels (numpy.Array): The corresponding labels.\n",
    "\n",
    "    Attributes:\n",
    "        X (torch.Tensor): The input features as a tensor.\n",
    "        y (torch.Tensor): The corresponding labels as a tensor.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, features, labels):\n",
    "        self.X = torch.tensor(features, dtype=torch.float)\n",
    "        self.y = torch.tensor(labels, dtype=torch.long)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.X)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.X[idx], self.y[idx]\n",
    "\n",
    "\n",
    "batch_size = 64\n",
    "train_dataset = ObesityDataset(X_train, y_train)\n",
    "test_dataset = ObesityDataset(X_test, y_test)\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 构建 MLP 网络\n",
    "\n",
    "我们创建一个多层感知机（MLP）网络，包含一个输入层、任意个隐藏层和一个输出层。其中，输入层的维度为 19，即数据集中的特征数；输出层的维度为 1*7，即某样本属于各类别的概率值。隐藏层的维度可以自行设置。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super(MLP, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size, hidden_size)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(hidden_size, hidden_size)\n",
    "        self.fc3 = nn.Linear(hidden_size, output_size)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.relu(self.fc1(x))\n",
    "        x = self.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "    \n",
    "input_size = X_train.shape[1]\n",
    "hidden_size = 64\n",
    "output_size = 7\n",
    "model = MLP(input_size, hidden_size, output_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练与测试\n",
    "\n",
    "我们将训练一个 epoch 的代码封装在`train_epoch`函数中，其中流程如下：\n",
    "\n",
    "1. 遍历训练集，将数据输入模型，得到预测值；\n",
    "2. 计算预测值与真实值之间的差值，然后计算损失；\n",
    "3. 通过反向传播更新模型参数。\n",
    "4. 返回训练集上的平均损失。\n",
    "\n",
    "然后我们实现了一个`eval_model`函数来评估模型的性能：\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Accumulator:\n",
    "    def __init__(self, n):\n",
    "        self.data = [0.0] * n\n",
    "\n",
    "    def add(self, *args):\n",
    "        self.data = [a + float(b) for a, b in zip(self.data, args)]\n",
    "\n",
    "    def reset(self):\n",
    "        self.data = [0.0] * len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.data[idx]\n",
    "\n",
    "\n",
    "def train_epoch(net, device, train_iter, loss_fn, optimizer):\n",
    "    # 将模型设置为训练模式\n",
    "    net.train()\n",
    "    metrics = Accumulator(3)\n",
    "    for X, y in train_iter:\n",
    "        X, y = X.to(device), y.to(device)\n",
    "        # 计算梯度并更新参数\n",
    "        y_hat = net(X)\n",
    "        loss = loss_fn(y_hat, y)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        metrics.add(loss * len(y), accuracy(y_hat, y) * len(y), len(y))\n",
    "    train_loss = metrics[0] / metrics[2]\n",
    "    train_acc = metrics[1] / metrics[2]\n",
    "    return train_loss, train_acc\n",
    "\n",
    "\n",
    "@torch.no_grad()\n",
    "def eval_model(net, device, test_iter, loss_fn):\n",
    "    net.eval()\n",
    "    metrics = Accumulator(3)\n",
    "    for X, y in test_iter:\n",
    "        X, y = X.to(device), y.to(device)\n",
    "        y_hat = net(X)\n",
    "        loss = loss_fn(y_hat, y)\n",
    "        metrics.add(loss * len(y), accuracy(y_hat, y) * len(y), len(y))\n",
    "    test_loss = metrics[0] / metrics[2]\n",
    "    test_acc = metrics[1] / metrics[2]\n",
    "    return test_loss, test_acc\n",
    "\n",
    "\n",
    "def accuracy(y_hat, y_true):\n",
    "    y_pred = y_hat.argmax(dim=1)\n",
    "    acc = (y_pred == y_true).float().mean().item()\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50 - Train Loss: 1.838430 - Test Loss: 1.707880 - Train Acc: 0.343125 - Test Acc: 0.415000\n",
      "Epoch 2/50 - Train Loss: 1.522666 - Test Loss: 1.316522 - Train Acc: 0.476875 - Test Acc: 0.517500\n",
      "Epoch 3/50 - Train Loss: 1.161792 - Test Loss: 1.031209 - Train Acc: 0.568750 - Test Acc: 0.592500\n",
      "Epoch 4/50 - Train Loss: 0.945682 - Test Loss: 0.882575 - Train Acc: 0.631250 - Test Acc: 0.642500\n",
      "Epoch 5/50 - Train Loss: 0.819996 - Test Loss: 0.782129 - Train Acc: 0.673750 - Test Acc: 0.680000\n",
      "Epoch 6/50 - Train Loss: 0.731772 - Test Loss: 0.702463 - Train Acc: 0.728750 - Test Acc: 0.722500\n",
      "Epoch 7/50 - Train Loss: 0.654994 - Test Loss: 0.633184 - Train Acc: 0.758125 - Test Acc: 0.752500\n",
      "Epoch 8/50 - Train Loss: 0.607451 - Test Loss: 0.579654 - Train Acc: 0.768125 - Test Acc: 0.775000\n",
      "Epoch 9/50 - Train Loss: 0.556200 - Test Loss: 0.552558 - Train Acc: 0.788125 - Test Acc: 0.775000\n",
      "Epoch 10/50 - Train Loss: 0.518987 - Test Loss: 0.517203 - Train Acc: 0.813125 - Test Acc: 0.795000\n",
      "Epoch 11/50 - Train Loss: 0.489930 - Test Loss: 0.497925 - Train Acc: 0.820000 - Test Acc: 0.787500\n",
      "Epoch 12/50 - Train Loss: 0.466644 - Test Loss: 0.478966 - Train Acc: 0.832500 - Test Acc: 0.805000\n",
      "Epoch 13/50 - Train Loss: 0.451705 - Test Loss: 0.456605 - Train Acc: 0.833750 - Test Acc: 0.815000\n",
      "Epoch 14/50 - Train Loss: 0.432746 - Test Loss: 0.448235 - Train Acc: 0.841875 - Test Acc: 0.810000\n",
      "Epoch 15/50 - Train Loss: 0.420262 - Test Loss: 0.429516 - Train Acc: 0.844375 - Test Acc: 0.807500\n",
      "Epoch 16/50 - Train Loss: 0.405777 - Test Loss: 0.423770 - Train Acc: 0.848125 - Test Acc: 0.812500\n",
      "Epoch 17/50 - Train Loss: 0.399022 - Test Loss: 0.411355 - Train Acc: 0.852500 - Test Acc: 0.822500\n",
      "Epoch 18/50 - Train Loss: 0.385543 - Test Loss: 0.425047 - Train Acc: 0.858750 - Test Acc: 0.830000\n",
      "Epoch 19/50 - Train Loss: 0.375786 - Test Loss: 0.436469 - Train Acc: 0.861250 - Test Acc: 0.797500\n",
      "Epoch 20/50 - Train Loss: 0.376939 - Test Loss: 0.417673 - Train Acc: 0.864375 - Test Acc: 0.807500\n",
      "Epoch 21/50 - Train Loss: 0.363838 - Test Loss: 0.427108 - Train Acc: 0.877500 - Test Acc: 0.830000\n",
      "Epoch 22/50 - Train Loss: 0.359189 - Test Loss: 0.415349 - Train Acc: 0.863125 - Test Acc: 0.822500\n",
      "Epoch 23/50 - Train Loss: 0.352268 - Test Loss: 0.398215 - Train Acc: 0.878125 - Test Acc: 0.825000\n",
      "Epoch 24/50 - Train Loss: 0.344535 - Test Loss: 0.393723 - Train Acc: 0.872500 - Test Acc: 0.830000\n",
      "Epoch 25/50 - Train Loss: 0.344794 - Test Loss: 0.389130 - Train Acc: 0.869375 - Test Acc: 0.832500\n",
      "Epoch 26/50 - Train Loss: 0.337921 - Test Loss: 0.378797 - Train Acc: 0.883750 - Test Acc: 0.830000\n",
      "Epoch 27/50 - Train Loss: 0.332188 - Test Loss: 0.389436 - Train Acc: 0.884375 - Test Acc: 0.837500\n",
      "Epoch 28/50 - Train Loss: 0.324991 - Test Loss: 0.378402 - Train Acc: 0.882500 - Test Acc: 0.840000\n",
      "Epoch 29/50 - Train Loss: 0.323235 - Test Loss: 0.374846 - Train Acc: 0.883125 - Test Acc: 0.832500\n",
      "Epoch 30/50 - Train Loss: 0.318025 - Test Loss: 0.377406 - Train Acc: 0.891875 - Test Acc: 0.847500\n",
      "Epoch 31/50 - Train Loss: 0.318651 - Test Loss: 0.371643 - Train Acc: 0.881875 - Test Acc: 0.845000\n",
      "Epoch 32/50 - Train Loss: 0.310555 - Test Loss: 0.380313 - Train Acc: 0.887500 - Test Acc: 0.835000\n",
      "Epoch 33/50 - Train Loss: 0.307738 - Test Loss: 0.374025 - Train Acc: 0.891875 - Test Acc: 0.832500\n",
      "Epoch 34/50 - Train Loss: 0.307275 - Test Loss: 0.390907 - Train Acc: 0.892500 - Test Acc: 0.842500\n",
      "Epoch 35/50 - Train Loss: 0.307407 - Test Loss: 0.381323 - Train Acc: 0.893750 - Test Acc: 0.837500\n",
      "Epoch 36/50 - Train Loss: 0.305127 - Test Loss: 0.395698 - Train Acc: 0.890625 - Test Acc: 0.827500\n",
      "Epoch 37/50 - Train Loss: 0.303896 - Test Loss: 0.370716 - Train Acc: 0.888125 - Test Acc: 0.860000\n",
      "Epoch 38/50 - Train Loss: 0.297611 - Test Loss: 0.372267 - Train Acc: 0.889375 - Test Acc: 0.847500\n",
      "Epoch 39/50 - Train Loss: 0.293867 - Test Loss: 0.363063 - Train Acc: 0.897500 - Test Acc: 0.845000\n",
      "Epoch 40/50 - Train Loss: 0.291327 - Test Loss: 0.391146 - Train Acc: 0.900625 - Test Acc: 0.837500\n",
      "Epoch 41/50 - Train Loss: 0.296458 - Test Loss: 0.376492 - Train Acc: 0.888125 - Test Acc: 0.850000\n",
      "Epoch 42/50 - Train Loss: 0.282158 - Test Loss: 0.372659 - Train Acc: 0.898125 - Test Acc: 0.862500\n",
      "Epoch 43/50 - Train Loss: 0.284454 - Test Loss: 0.365779 - Train Acc: 0.904375 - Test Acc: 0.857500\n",
      "Epoch 44/50 - Train Loss: 0.284954 - Test Loss: 0.355868 - Train Acc: 0.901250 - Test Acc: 0.852500\n",
      "Epoch 45/50 - Train Loss: 0.281577 - Test Loss: 0.380048 - Train Acc: 0.901250 - Test Acc: 0.837500\n",
      "Epoch 46/50 - Train Loss: 0.277218 - Test Loss: 0.358737 - Train Acc: 0.893125 - Test Acc: 0.865000\n",
      "Epoch 47/50 - Train Loss: 0.271943 - Test Loss: 0.367363 - Train Acc: 0.907500 - Test Acc: 0.850000\n",
      "Epoch 48/50 - Train Loss: 0.271222 - Test Loss: 0.393174 - Train Acc: 0.910000 - Test Acc: 0.840000\n",
      "Epoch 49/50 - Train Loss: 0.270641 - Test Loss: 0.365483 - Train Acc: 0.903750 - Test Acc: 0.850000\n",
      "Epoch 50/50 - Train Loss: 0.263705 - Test Loss: 0.356542 - Train Acc: 0.908750 - Test Acc: 0.850000\n"
     ]
    }
   ],
   "source": [
    "epochs = 50\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = model.to(device)\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "train_ls, test_ls, train_acc_ls, test_acc_ls = [], [], [], []\n",
    "\n",
    "for epoch in range(1, epochs + 1):\n",
    "    train_loss, train_acc = train_epoch(model, device, train_loader, loss_fn, optimizer)\n",
    "    test_loss, test_acc = eval_model(model, device, test_loader, loss_fn)\n",
    "    print(\n",
    "        f\"Epoch {epoch}/{epochs} - Train Loss: {train_loss:.6f} - Test Loss: {test_loss:.6f} - Train Acc: {train_acc:.6f} - Test Acc: {test_acc:.6f}\"\n",
    "    )\n",
    "    train_ls.append(train_loss)\n",
    "    test_ls.append(test_loss)\n",
    "    train_acc_ls.append(train_acc)\n",
    "    test_acc_ls.append(test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画出模型在训练集和测试集上的 Loss 和 Acc 曲线。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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M4KOPPuLmm29m9erVrFixgnHjxtX5+fz22298+umnXH/99aXbBg0axKRJkxgyZAi7d+/mP//5D8899xyOjg1762PXrl28+uqrAPTp04e1a9fi6+tb2j5mzBguvvhipkyZQmFhIbNmzeKPP/4obc/Pz2fRokUAPPDAA5UqRwFMnTqVZ599lrS0NJvt33zzDRaLBQ8PD37//XdCQkJs2kePHs2tt95KZmYm7u7uDfWUpZ42btzI2rVrAbjllltsglJnPPDAA3z00Ufs27ePN998k8cffxwnp5p/yvPHH38kOTkZMIJZ5YNSZ3h7e/Paa68xYcIEkpKSmDdvHrNnz67jsxIREREREZHmVFxi4Y/DaSzemcjSPSdIy7G9d2Y2QecADyJDPIkK8SIyxIuoEE8iAj1wcWx7Iar8ohKe/n4PX22Or9Tm7+HMk5f2pHcHH8L83HFzbrivj4+bE1cO7siVgzuSmVvEsr0nWLIrkXXRqRRb7Aen8ossLNmVyJJdiTiaTVXuVxUvF0fO6xrA6NMBqS6BHmcNeLVFbTYsVfGGYmP617/+xeOPP05JSYnN9tTUVFavXs3q1at54403WLRoEQMGDGiycbUWkcGe/LI3qXQ9JjmrGUcjIiIiIiJNLT23kMHPL2/uYTSpLU+MJ6AFfcrroYceqlQdqSJ7QanyZs6cyZw5c9i+fTsLFy6sV1jqiiuusAlKneHi4sLs2bO5/fbbOXnyJHv37qVfv351Po897777bukUeB988IFNUOqMiRMncvPNN/PBBx+UfkjrTLgsLS2NoqIiAC644IJqz+Xv72+zfuLECcAIulV3X8PHx6fGz0ca38KFC0sfz5w50+4+ZrOZG264gUcffZSMjAxWrlzJxRdfXONzlA9CTpo0qcr9xowZg6urK/n5+XzzzTcKS4mIiIiIiLQiJRYrfxw+yZKdify8+wQnc6r+cKHFCodScziUmsPSPWXvtTuYTXQOcC8NUEUGG2GqiEAPnB3NTfE0mtzRk7nc8fkW9hw/ValtYCdf3pkxiPY+bo0+Dh93J64eEsbVQ8LIyC1k2R6j4tT6mKqDUzUJSjmaTQzs5Mv53YI4PzKA/h19cXQ4N7+XDanNhqXK69SpEz169GDZsmUN3vcXX3zBI488AoCzszN33XUX48ePJzAwkNjYWN555x3WrVtHfHw8EydOZP/+/XZvtLZlkSG2JeFVWUpERERERKRpzZgxo1b7W61WkpKSOHXqFIWFZTfuQkND2b59Ozt27Gi08QwePLj08aFDhxo8LLV8uRHc6927N8OHD69yv9tuu40PPvig9JgzYamAgACcnZ0pLCzk008/ZfLkyTWufnVm2sO9e/eyceNGhg0bVp+nIk1k3bp1gFHhrfzrs6ILL7yw9PH69etrFZY6Uy0dqv+AoKOjI/7+/hw/fpzff/+d4uLiBq++JiIiIiIiIg2nxGJlU1waS3Ym8tPuRFKz61d9vcRiJTYlh9iUHH7afaJ0u6PZROdAD6JCPIkM9iIqxIsOvq4NVpHI0WyiW7Bnk08NuHxvEv83f7vdqetuGtmZxyb3bJaQmK+7M9OGhjFtaBjpOYUs23uCxTsT+S32JCU1CEhFBnsyqlsgoyMDGd4lAE8X/W1fW232K/bUU08xdOhQhg4dSkhICHFxcURERDT4ef7xj3+UPl6wYAFTpkwpXR82bBjXXnstV155JQsWLCApKYkPPviABx98sMHH0ZpFBttOT3g0LZf8ohLNsSoiIiIiItIEPD096dKlS432XbJkCe+++y5r1qwhK6vqqsCpqan1GlOPHj2qbCtfjam6MdRFQUEB0dHRANUGpQAGDhyIk5MTRUVF7N69u3S7i4sL06dP59NPP+Wbb75h06ZNTJs2jTFjxjBy5MhqP0B17bXX8s9//pOCggJGjRrFxIkTmTJlCueffz69e/dWSfUWat++fQB069at2mBS+df1mWNqytOz7INmmZmZVe5ntVo5dcr4JG1hYSExMTHV/jzVy8mT4FKHCnmenuBWxSd6U1PBWrvpB0q5u4OHh/22tDSoUBG+xlxdwcvLfltGBpyuJFdrzs5QVZW4zEworOMbJE5OUNV1JisL8vPr1q+DA1SohlcqJwdyc+vWr8kEgYH22/LyILseH6oMCrK/vaAATlX+xHmNBQSA2c6bLYWFxveurvz8wN41pLgY0tPr3q+Pj/F6q8hiMX6O68rbu+prQEpK3fvVNcKga4RB14gyukYYdI0w6Bph0DWiTBu8RuQXlXA0LZfY5GyOnMzF282RqaOi8PLztn9AC7tGlFisbE3K5YfD2fy0+wQpWQU27d752ThaKvfbLciTnIJiEk9V/fNY5OBIlkvl8RZbrCTFJ5EWd4wNtR4xFJsdOOXqabfNoyAXl5IiPJ0d+PPAUK4ZGkaon3vNOq7jNaK4xMK7q2L56Lc4HIHyPbg5mXlyah8mjultv98mvkb4AdM7uzG9cxfSczqy8kAyv+xNYvOR9NLglL+HM337RjCqewjndwuknY9rWQeFhfX7/7M1XCPqc02pQpsNSz377LONfo5Tp06xZ88eAAYNGmQTlCrv6aefZsGCBQD8/vvvjT6u1qZrkCcmU9n/T1YrxKZk07uDphUQERERERFpbDWpfmy1Wrntttv473//W6M+8/Ly6jUmd/eqb6iZy91YLqnrmxZVSC93gyg4OLjafZ2cnAgICODEiROkpaXZtM2dO5eMjAx++OEHjhw5wssvv8zLL7+M2Wxm0KBBTJs2jVmzZlWaTq9Hjx588cUX3HbbbaSnp7N48WIWL14MQGBgIBMnTmTWrFmMHj26gZ6x1Fd+fn5pOLBjx47V7uvn54eHhwc5OTnEx8fX6jw9e/Ysfbx69eoqK1ht27aN7HI3fI8ePVqrsFRCQkK17YmJiWUrAwbUuF8bc+fCXXfZb+vZ03gToy6efhqeecZ+2+jRsHdv3fq98054+237bZddBqtX163fq66Cr7+233brrfDNN3Xr98ILYdUq+22PPALvvFO3fnv1gtP3QSt5+WWo673YwMCqb/p/+CHUZyrJqt4MW7QIpk2re7/JyfbfHPntNxg7tu797t4Nve28mXPgAPTpU/d+V66EMWMqbz95Es7yf1215s+Hq6+231affnWNMOgaYdA1ooyuEQZdIwy6Rhh0jSjTBq8RrkDU6eWMV/90N8NfeYrzI+2E6FrYNcIB2DdwCp9cfIfd9vcXPM958bvttp3Nku6juOuyR+22vfjTHKYcWF+nfjeE9eGa61602/bw6o+5YduSOvVb12uEI3D36cWuT1rmNcIPuOL0UomuEQ2qzYalmkL5qQaq+xRu165d7R4jBjdnBzr6uRGfVnYzPSZZYSkRERERkbbCz92ZLU+Mb+5hNCk/dzufumomDg5nr+r74YcflgalBgwYwH333cfw4cMJDQ3F3d29tI8bbriBTz/9FGtdP63ZgtSnipO3tzeLFi1i48aNzJ8/n1WrVrF9+3ZKSkrYvHkzmzdv5pVXXmHhwoWMGDHC5tgrr7yS8ePH89VXX7F06VLWrl1LSkoKqampfPbZZ3z22WfceOONfPjhhzbBMWke5aubla/+VJUzYansWn6CddKkSTg6OlJcXMxrr73GDTfcQGCFT9FbLBYef/zxKsdXE2FhYbXaX0REREREpCEUFluIO5ljE36qrYy8Iq7/7x/MGN6JRyf3bNXTlrk7133sI7sG8K8r+3IwKZuDSVlEJ2VzoppKVCLnqtZ7BWgFAgMD8ff3Jy0tjUOHDlW5X2xsbOnj7t27N8XQWp3IYC+bsFR0Uj3K3omIiIiISKtiNpsI8KzDVE7SZP7zn/8AxjRjv/32G25VTH1RscJSa+Pn51f6OCkpqdp9i4uLOXm6RLh/FeXihw0bxrBhwwAjtLJq1SrmzZvHggULSE5O5sorryQ2NrbS19PHx4dZs2Yxa9YswJiy7fvvv+ett97i+PHjfPzxxwwcOJB77723zs9VGkZ+ualInO2Vnq/A5fRUE7WtvhYWFsbtt9/O3LlzOXbsGKNGjeKll15i7NixODs7s337dp555hmWLl2Ks7Nz6Yf16lvlTUREREREpKFl5hWxbmeiEeRJzuJgUjZxqTkUW6zENUD/n/9xlNUHU3jpqn6M7FrFVI0tUM/23lzarz2T+7YnYoMPxJ79GHv83J2ZPrSTzbbMvCJikrMI2uQDB+rWr9lswt3Z/gfuHB3q/oEzkcaisFQju/3223nhhRfYunUrP//8MxMnTqy0z3PPPQeAo6Mjt956a1MPsVWIDPbk1/3JpevRybX75KOIiIiIiIjUrxpSdc5MQf+nP/2pyqCU1Wpl69atjXL+puLi4kJkZCTR0dH88ccf1e67bds2ioqKAOhTg5LlXl5eTJ06lalTp3LvvfcyZ84cEhMTWbduHRMmTKj22J49e9KzZ09mzJhBz549ycnJYf78+QpLtQCurq6lj2tSTbygoACgyp+j6rzyyiscOnSIH3/8kYMHD3LZZZdV2mfIkCEMHTqUd999FzBed7VxtukBExMTSwOAIiIiIiIidfHEd7v4YW/jzjCUkJ7Hdf/5gxtGhPPwxB54NOrZ6q5HOy+m9G3P5H7t6Rp09mrFdeXj5sTgcH/wd69zH8M6+7P375WzEACc/AE217lrkUahsFQje+yxx9iyZQtLly7lsssuY/bs2YwbN47AwEAOHTrEu+++y+rVq3FwcGDu3Ln06NGj1udISEiotj0xMbGuw28xugbbXvyjk1VZSkREREREpLbKBzfOhDIaQnFxMQA5OTlV7vP999+fE3+fjh8/nujoaPbs2cPGjRurDIZ88MEHNsfUxrhx45gzZw4AqampNT4uLCyMqKgotm3bVqvjpPGUDyPVZGq9Mz9DNZmyryIXFxd++OEHPvzwQ95++2127NhROuVlcHAwt912G0888QT3339/6THlq6XVRMeOHWu+8/bt0KFDrfoHoLrnvm8f1HUaT/dqbvqvXQslJXXrt9x1tZKFC+F0aLLWqqtE9sEH8M47devXyanqthdfhGeeqVu/1U3Z+re/wV131a3f6kK+N98M06bVrd/q/OlPkJx89v2qEhBgf/vIkfXrt6qf1+7d69evTxVvRAYE1K9fb++q2+rTr64RBl0jDLpGlNE1wqBrhEHXCIOuEWVa8DWiuMTCr/uT+d8fR9h57BQA2c5V/8wNuvtzu9tDvFzoEuxJ10APugZ50iXIg4ggTzxdHLFYrHyzJZ4f1lR+T/2T34+w6kAKr/+0nsHhtfv7qFQNrhFWq5X1sanM/TWWg0nVFwXpFuTJhF4hXDSwEzd0aW9/p1Z8jcjOL2LRjuN8uSmehPTqqx0P7OTLdSM6M8ZixcFs52f6b38j9uobefCbHRw5mVupeViEHy9c1hd/exXrdY0o04KvEaWOH4cBA+o+FjsUlmpkHh4eLF68mE8++YQXXniBV199lVdffdVmnyuuuIJHHnmEoUOH1ukcYWFhDTHUFi2yQljqyMlcCopLcHGs5hcoERERERERsdG+fdlNttjY2AabCj4yMpJdu3bxww8/8MILL1Sadi42Npa76nqDu4W54447eO+997BYLMyaNYs1a9bgXeHNnmXLlvHf//4XMKbaK//3/qFDh4iPj+fCCy+s8hzLli0rfRwREVH6eOHChYwZMwZfX1+7x8XHx7N///5Kx0nzcXV1JSAggJMnT571w27p6emlYam63usxm83ceuut3HrrrWRlZZGUlIS7uzvt2rXDbDYDEB0dXbp/r1696nSeGgkIgKCghu0zsJGmyKhiqsx6q+Jntd6quuFcX15extLQPDyMpaG5uRlLQ3NxafjXLhhvSjVGv46OjdOv2dw4/ULj9atrhEHXCIOuEQZdI8roGmHQNcKga4ShGa8RGbmFfLkpnk9+i+N4Zj5gAvezvz6d24UQGeJJVIgXUSGeRIZ40S3YE2/XqoM6ZmDa5GCGD+/B377ZycbDaTbtR9NyufKbaGaO6sxDl/TArYqp5OrE359tR9P518/72XAozRiNnefZLdiTS/u1Z0rf9kSG1OBnqRVfIzyB68I6MH3yYFbuT+bD9Yf5Lfak3cNWpFpZ8cNhOq47wY0jOjNtaBg+bmXf6++jM3jk2wPkFTlV+rrOHtuN+ydE2Q9ZnY2uEYaW9HtEA37o8wyFpZrAxo0b+eyzzzh06JDd9l9++YWgoCCioqLwaawLUCvXrUJYqsRiJS41l+7tGuEXLxERERERkXPUwIEDcXV1JT8/nyeffBInJyfCw8NLQxShoaF1mv7rhhtu4G9/+xvHjx9nxIgRPPzww/Tp04f8/Hx+/fVX3njjDQoKChg0aFCrn4qvb9++PPDAA7z88svs2LGDQYMG8fDDDzNw4EBycnL44YcfmDNnDiUlJTg7O/Pee+/ZHH/06FHGjh1Lr169uPzyyxkyZAihoaGAEXb66quvmD9/PgADBgxg+PDhpce+8cYbzJgxgylTpnDRRRfRs2dPfHx8SE9PZ/Pmzbz11lvk5Rmfyrz99tub6CsiZ9OrVy/Wrl1LTEwMxcXFODravx13JugGxtSK9eXl5VVpmr2SkhK2b98OQJcuXQhsrDcNRURERETaGKvVSn6Rhcy8IpuluMRCpwB3ugZ54uqkIhAxyVl8tD6Ob7cmkF9kqXI/VyczQ8L9bYJR3YK9bIIytRUe4MGXt53Hx7/H8a+f91c6/0fr41i5P5mXr+7P0M71D0LGpmTz8s8H+HnPiSr3GRLux8OTejTI+VobB7OJ8b1CGN8rhH2Jp5i3Po7vth+jsLjy6yIhPY9//LiP15cf5OrBHZlxXjifbTjCJ78fqbSvj5sTr0/vz0U9QpriaUgrprBUI/vmm2+4/vrrKSgooF+/fjz77LNccMEFeHl5ld4Efe6553jvvfdYs2YNy5cvp0MtS5THx8dX256YmFjltACthZerE+19XEnMzC/dFp2cpbCUiIiIiIhILXh5eXHPPffw0ksvsXXrVi6++GKb9pUrVzJmzJha93vvvffyyy+/sGzZMg4ePMgtt9xi0+7m5sYnn3zCkiVLWn1YCuDFF18kJyeHd955h9jYWGbNmlVpHx8fH+bPn8+AKkqE7927l71791Z5jh49erBgwQJMFcrC5+bm8vXXX/P111/bPc5sNvPss89y2WWX1fj5SOM6//zzWbt2LTk5OWzZssUmAFfe6tWrSx+PGjWqUcaycuVKTp40PrE7ffr0RjmHiIiIiEhrZbFYKSi2kJVvG3jKyLVdP5VXREaFUFRmXpHdkMcZZpMR1okMNsI/Z0JAXYI8zvmZdCwWK2uiU/hwfRxrDqZUu287b1duGBnOtUM74edRzXRxdWQ2m5g5KoIx3YP529c72Hwk3aY97mQu0977nVtGRfDgJd3rFHA7kZnPmysOMn9zAiUW+9N/RgZ78tDEHozvGVzp7/62qGd7b/51VT8emtid//1xlE83HCE5q3IlodzCEj7+/Qgf2wlJAfQJ9ebdGYMJ869makSR0xSWakRJSUncdNNNFBQU0Lt3b3777Tc8ypWK7NKlC48++ijDhg1jwoQJ7Nu3j7vvvptvv/22Vufp2LFjQw+9ReoW7GkblkrKbsbRiIiIiIiItE4vvvgikZGRfPLJJ+zZs4fMzExKSkrq1aeTkxNLlizh3Xff5ZNPPmHv3r1YrVZCQ0MZP3489957Lz169GDJkiUN9Cyal9ls5u233+aaa67hvffeY+3atSQlJeHi4kKXLl2YPHky9913H0F2SoqPHj2aVatWsXTpUjZs2EB8fDxJSUnk5+fj7+9P//79ueKKK7jppptwcXGxOfaLL75g8eLFrFq1ir1793LixAlSU1NxdXUlPDycCy64gNtvv51+/fo11ZdCauCyyy7jn//8JwAfffSR3bCUxWLhk08+AcDX15exY8c2+DisVivPPPMMYPzM3nbbbQ1+DhERERFpGU5mF7ApLo3wAA96tvc++wGtVE5BMduOZnA0LZf8ohLyi0soKLKU/ltQbj3/9Lrdf4tKyC+2VBt2qi+LFQ6n5nA4NYdle5NKtzuYTYQHuBMVXDatXFSIFxGBHjg7mhttPE0ht7CYb7ce46P1hzmUklPtvgM7+XLzqAgm9mmHk0PjP++IQA+++usIPlp/mJeXHqCg3PfeaoUP1h3m19NVpgaH+9Woz8y8Iv69OpaP1h+usmpWBx9X7p8QxRWDOtZterhzXICnC3ePi+SvF3blx12JfLj+MDsTMmt07LXDOvH01F6q4CY1ZrJarfbjjG1MXFwcERERANx4443Mmzev3n2++eab3HfffQB8/vnnXHfddVXuO2HCBJYvX47ZbCY1NRU/v5pddGsiISGBsLAwwKhC1VrDVX//YS8frj9cuj6lb3venjGoGUckIiIiIiL1FR0dXTotVWRkZHMPR6TNqsvP4rlyv6EpXHDBBaxduxZHR0fWrFnDiBEjbNpffvllHnroIQCefvrp0lDTGatWrSoNUFV13+rkyZN4enpWCtmBMf3ePffcwzvvvAPAU089xbPPPtsAz8yWXhMiIiIizSs+LZf/rD3EV5viS8MfF/UI5qGJ3enRrvWHppKz8tkcl86muDQ2x6WzN/FUlZV7WjsHs4nOAe6nq1AZQaqoEC86B7T8ENWxjDw++S2OLzYe5VR+cZX7OZpNTO7bnpmjOjOwU8O9N15bsSnZPPj1DrYdzajUZjbBbaO7cP+EqCpDOPlFJXzyexxvr4wlM6/I7j4+bk7MHtuNv4wIV5inFqxWK1uOpPPR+jh+2p2IvR93F0czz1/Wh6uHhDX9AKXJNMb9BlWWakT79u0rfTxoUPWhnsGDB7N8+XIsFgsHDx6ssiR7WxYZ4mmzHp2c1UwjERERERERERGpuTfffJNRo0aRl5fHxRdfzGOPPcbYsWPJy8vjyy+/5P333wcgKiqKBx54oE7nWLlyJbNnz+aaa67hwgsvpFOnTuTn57Nz507ef/99tm/fDsCkSZN4/PHHG+qpiYiIiEgLsP/EKf69KpYfdiZWCg/9uj+ZlQeSuXxgKP83IYqOfq1jeiqr1cqh1Bw2HU5jU1w6m4+kceRkbnMPq8a8XB3xcXPCajXCQ7VVYrESm5JDbEoOP+0+UbrdbIL2Pm508nc3lgD30sfhAe74uDk1y7Ru5UMtP+85UW2IzdfdieuGdeIvI8Jp7+PWhKO0r2uQJ9/cPpL/rjvEK8sO2lQYs1jhvTWHWL4viVenDWBAmG9pW4nFyrdbE3j9l4M2syOV5+pk5uZREfz1wq74uDk19lM555hMJoZ09mdIZ38jhPd7HF/8URbCCw9w590Zg+nVofWHQaXpKSzViBwdy768xcVVp2YBiorKUqblj5MykcG2YanDqTkUlViapBSjiIiIiIiIiEhdDRw4kK+++orrr7+eU6dO8dhjj1XaJyoqiiVLluDl5VXn8yQlJfHmm2/y5ptvVmozmUzMnDmTd955B2dn5zqfQ0RERERajs1xaby7KpYV+5Or3c9qhQVbj7F4RyJ/GRHOXWO74e/Rsn4nLCy2sOd4ZlnlqCPppOUUNuuYPF2MwJO3mxM+bo74ujnj4+aEj7tT6XZfN+Nx+cXbzclmirWcgmJikrM5mJRF9Jl/k7LrFKKynA5fHcvI4/dDJyu1e7k6lganws6EqPw96OTvTgdfVxxr8b5qcYmFU/nFZOQWkplXVLqcOv1vRm7ZtqNpuew/UX2hi8hgT24+P4LLBoTi5tyyqis5mE3MuqArF/UI5oGvd7IjPsOmPTYlhyveWc9fL+zKveMiWRudystL93MwKbvK/qYNCeO+8ZGEeLs2wTM494X6uvHopJ7cOy6S1QdSKCi2cEnvdi3utSSth1I5jejMtH4Aa9eupU+fPlXuu2bNGsC4cdW5c+fGHlqr1K1CWKqoxMqRk7mVtouIiIiIiIiItDRTp05l586dvPnmmyxZsoSEhAScnZ3p1q0bV199NbNnz8bdve6f8h89ejQvv/wyv/76K/v37ycpKQmz2UyHDh0YO3YsM2fOVCVzERERkXOA1Wpl1YEU3lkVw6a49FodW1hi4b/rDjN/UzyzLujCLaMjcHdunreLs/KL2Ho0g81xaWyKS2N7fAb5RZazH1iBo9lEj/ZeeLs64erkgKuTGRfHsn9dyq27nl4v/6+rk+02F0czXq5OeLs61ipYVB0PF0f6h/nSv1xVIoDsgmKiTwenDiZlcTA5m+ikrCqrFNVEVn4xe46fYs/xU5XaHMwmQn2NqlRh/u6EeLuQW1hCZm4RGXlnAlHFpWGo7ILqi4HU1EU9grl5VASjugU0S9Wr2ugW7MW3t4/g/bWHeOOXaApLbKtMvbsqls82HCGrmukFJ/Vpx4OXdKdrkN7Dbgzuzo5M6tu+uYch5wCT1Wo9NydyraW4uLjScNONN97IvHnzznrMvHnzmDlzJgBPP/00zzzzjE37/v376dWrF1arldDQUP744w9CQ0Mr9fP+++/z17/+FYARI0bw22+/1e/JVNAY8zc2l6H/WE5KVkHp+r+vH8TEProYioiIiIi0VtHR0RQXF+Po6EhkZGRzD0ekzarLz+K5dL9BGoZeEyIiIiKNp7jEwpJdiby7KrbaCj5uTg5cO6wT1w3vxJKdiby/JpacwhK7+wZ5uXDPuEiuGRrW6DO5lFisbI/PYPm+JNYcTGFf4imqmamtSl4ujgwK92NoZz+GdPanf0ffc66yzKn8IqKTjODUwaRsopOzOJiURdKpgrMf3EK4Oztw9eCO3DiyM11aaWjowIksHvx6B7uOZdZo//O6+PPIpJ42U/WJSMNojPsNbbay1Lp164iJiSldT01NLX0cExNTKSx100031focPXr0YObMmXz44YccO3aMgQMHct999zF69Gi8vLyIj4/nyy+/5H//+x8ADg4OvPDCC3V6Pm1FZLCnTVgqOimbiVUX7BIRERERERERERERERFptfKLSvh6SwLvr4klPq3qadt83Z24aWRnbhzRGb/TU+zdOz6SGed1Yu6vMXz+xxGKSmzTSSlZBTy5cDf/XXuIBy/pzuQ+7TGbG67yT05BMWujU1m+L4mV+5M5WYdp9UK8XRja2Z+hnf0Z0tmPHu28baa4Oxd5uzoxONyPweF+Ntsz84qIT8vlaFouR04a/x5Ny+FoWi7HM/IpqUv6rIGF+roxc1Rnrh4Sho+bU3MPp166t/NiwZ0jeW91LG+uiK7083NGz/bePDyxOxdGBbX4ylkiUqbNhqU++OADPv74Y7tt69evZ/369Tbb6hKWAnjnnXfIycnhq6++IiUlhccff9zufh4eHrz//vuMGTOmTudpKyKDPfkttmz+3ehk+/PAioiIiIiIiIiIiIiIiLRWmXlFfLbhCB+tP0xqdtUho/Y+rtw2ugvXDAuzO6VeoKcLz/ypNzePiuC1Xw7w/Y7jVJx3KO5kLrP/t42+oYd4ZFIPRnULrPO4j2fksWJfEsv3JfN77EmbacxqIjLYk6ER/kblqHB/Ovq5KYBymo+bEz6hPvQJ9anUVlRi4XhG3ukAVS5HS8NUxuOsek6p5+JoNs5ffnG3Xe8a5MnIrgENNn1hS+DkYGb2RZGM6xnCA/N3sDexbHrDMH83HpjQnT/179CgIUMRaRptNizVVFxcXPjyyy/561//yrx589iwYQPHjh2joKAAb29vunfvzvjx45k1a5ZKk9dAtxAvm3WFpURERERERERERERERNoOi8VKfnEJBUUWm3/ziywUFJWQX1z2b35RCQWn1wtOr5tNJoK8XAjxdiXE2/g3wMO5xQQ8krPy+XBdHJ9vOFJtwKVrkAe3X9iVPw8Ixdnx7GPvFODOG9cM5LYLuvDSzwdYfTCl0j67jmUy44M/GB0ZyMMTe9gN5VRksVjZfTyT5XuNgFT5MMnZODmY6NfRlyGd/Rga7s/gcL/SqlhSO04OZsIDPAgP8KjUZrVaycgtMipSpeUSn5bLkZM5pOUU4eXqWDkEVS4I5evmhLebE65O59ZUh7XVs703388exZeb4tkSl8awiACuGtyxRj97ItIymazWitlhOdc0xvyNzWXDoZNc8/6G0nVnRzP7/j7xnC+3KSIiIiJyroqOjqa4uBhHR0ciIyObezgibVZdfhbPpfsN0jD0mhAREZGGVFBcwvurD/HDzuOk5RSVBp5qW6moJswmCPB0McJTXq4Enw5SBXuVBaqCvV0I8HCp93tSVqv1dIDLNuyVlV/Mt1sT+GZLAoXFVT/H/mG+3DmmKxN6htSrms1vsan86+cD7IjPqHKfqf078ODFUZUCOPlFJayPMabXW7EvmeSsghqfNyrEk3E9Q7gwKogBYb5tPoQjIiJn1xj3G1RZSlqVyGBPm/XCYgvxabl0DqyckhYREREREREREREREZHW51hGHnd+vrXaIE9DslghJauAlKwCdlN1ZSQHs4lAT2cjPOXlSpCXC2AEuwqKLBScrnCVX66S1Zl/S6tcVROEqs7oyEDuGNOVEV0CGmRaupFdA1l4ZwA/7z7By0sPcCg1p9I+P+w4zk+7Erl2WCeuPy+cbUfTWb4viXUxqeQX1ex5OJpNDO/iz7geIYzvGUKnAPd6j11ERKS+FJaSViXA0wV/D2fScsrmZo5OzlZYSkRERERERERERERE5Byw5mAK9365jfTcouYeSiUlFitJpwpIOlUAZDb6+UwmmNy3PXdc2LVGU+LVvn8Tk/q2Z0KvEOZvTuCN5QcrVYkqtlj5dMMRPt1wpMb9+rg5MbZ7kFFBqnsQ3q5ODT10ERGRelFYSlqdbsGebDycVroek5zNhF4hzTgiERERERERERERERERqQ+Lxcpbv8bwxoqDWK21P97VyYyrkwMujrb/ujo64OJkxuX0v4XFFpKzCkg+lU9KVgHFljqcrJE5O5i5cnAosy7oSkQTFAxwdDBz3fBOXD4wlA/XH+bfq2PJyi+uVR9dAj0Y1zOYcT1DGBLuh6ODuZFGKyIiUn8KS0mrE1khLBWdnNWMoxEREREREREREREREZH6SM8p5P7521l1IKVSW3iAO49P7om/h7NtGOp0AMrVyYyzg7lOU9NZLFbScgtJOpVP8qkC498s49+kUwUkZ+WTdCqf1OxCSho5VOXsaCbI04Up/dpzy/kRhHi7Nur57HFzduCusd24blgn3l0dy7zf4iisYtpAswmGdPZn/OmAVNcgzyYerYiISN0pLCWtTmSw7S9bMcnZzTQSERERERERERERERERqY8d8Rnc+flWjmXkVWqb0CuEV67uj49b40zjZjabCPR0IdDThd4dqt6vxGLlZE5BaaDqTJAqNbsAB5OpNMTlUr6ilb0qV+UCXmf+dXVywNnBjNlc+7BXY/HzcOaxyT25cWRn3vjlIN9uTcBiBS8XRy7oHsT4nsGMiQrGz8O5uYcqIiJSJwpLSasTGeJlsx6TnI3FYm1Rv0SKiIiIiIiIiIiIiEjrZbVaiUnOxmSCbsFeZz+gCf24K5FXlh4gv6iE87oEML5XCKMjA/FybZxAUWOxWq38b+NRnl20l8IS2+pFZhM8NLEHf72gS50qRjU0B7OJYC9Xgr1c6RPq09zDaTKhvm68fHV//nZJd5JOFdC9nRfOjppeT0REWj+FpaTVqVhZKrewhOOZeXT0c2+mEYmIiIiIiIiIiIiIyLnieEYeD369g99iTwIwvmcIr17dHx/35g0jlVisvLR0P++tPlS6bcG2YyzYdgwnBxPndQlgXA9jSrQw/5b9nkleYQmPf7eLBduOVWoL9HThrWsHMqJrQDOMTOwJ9nYluBmmBRQREWksCktJqxPk5YK3qyOn8otLt0UnZyssJSIiIiIiIiIiIiIi9fLDjuM8/t0um/cglu9L4tK5a3l3xuBmqyqUkVvI3V9sY210qt32ohIra6NTWRudyjM/7KVHOy/G9QxmfM8Q+nf0bVGzcxxOzeGOz7aw/0RWpbahnf2Ye90gQhTMERERkUaksJS0OiaTicgQL7YcSS/dFpOUzdjuwc04KhERERERERERERERaa1O5RfxzPd77FY6AohPy+OKd3/juT/3ZvrQTk06tgMnspj16WaOnMyt8TH7T2Sx/0QWb6+MJdDThYt6BDGupzFdn7tz8709+PPuRP729U6yCoortd02OoKHJvbAyUHTvImIiEjjUlhKWqVuQZ42Yano5MqfPhARERERERERERERETmbjYfTuP+r7RzLyKt2v8JiCw9/u4vNcek8d1kfXJ0cGn1sP+9O5P/m7yC3sMRmu5ODiRtHdOZAUhYbDp2kqMRaZR+p2QXM35zA/M0JODuaGdU1gHE9QxjXM5j2Pm6N/RQAKCqx8NLP+/nP2sOV2jxdHHn5qn5M6tu+ScYiIiIiorCUtEqRIZ4269HJ2c00EhERERERERERERERaY0Kiy28ueIg766KxWIna3Rpv/YkZubbfHgb4OstCew+fop3Zwyic6BHo4zNYrHyxvKDzPk1plJboKcL/75+EEM6+wOQlV/E2uhUlu9NYuWBZNJzi6rst7DYwsoDKaw8kMITC6FPqDfjeoQwvmcIvTt4N8p0fcmn8pn9v21sjEur1NY9xIt3rx9ElyBPO0eKiIiINA6FpaRV6hZs+0tzTFI2VqsVk6nlzLktIiIiIiIiLcOqVasYO3YsACtXrmTMmDHNOyARERERaXaxKdnc9+V2dh3LrNTm5eLI85f34c8DQikqsfDPH/fz4Xrbikj7Ek8xde46Xr26Pxf3btegYzuVX8T9X25nxf7kSm39w3x57/rBtPNxLRuvqxOT+7Znct/2lFisbD2azvJ9SSzfm0RsSk6159p97BS7j53izRXRuDs70C3Yk8hgL6JCPIkK8SIyxJNQX7c6v/+y4dBJZv9vG6nZBZXaLh8Yyj8u79Os0wKKiIhI26TfPqRVigzxslnPKigm6VSBzR8HIiIiIiIiIiIiIiIi5VmtVj7/4yjPL9lLfpGlUvuwCH9em9afjn7uADg5mHlqai8Ghfvy8Dc7ySk3HV5WfjGzPt3C7Rd25cGLo3B0MNd7fLEp2dz2yWYO2Qk5XTW4I8+fZfo/B7OJoZ39GdrZn0cn9SQuNYfl+5JYsS+ZjXFplNgroXVabmEJOxMy2ZlgGyDzcHagW4gXUcFlAaqoEC/a+7hWGaKyWq28v+YQLy09UOmczqe/pjOGd9KH4EVERKRZKCwlrVIHH1c8nB1s/iiJTs5SWEpERERERKQa8+bNY+bMmQAcPnyYzp07N++AqnHmTZOnn36aZ555pnkHIyIiIiLnhNTsAh7+Zqfdik1ODib+b0J3Zl3QBQc7U9Fd2q8DPdp5c8dnW4hOzrZp+/fqWLbHp/PWtYMI8nKp8/hW7Evivi+3k1VQbLPdwWziqUt7ccOI8FqHizoHenDr6C7cOroLmblFrDqYzIp9yaw6kMyp/OKzdwDkFJawIz6DHfEZNtu9XBzpFuJJVHBZgCoqxAt3FwcenL+DZXuTKvUV6uvGOzMG0T/Mt1bPQ0RERKQhKSwlrZLJZKJbsCc7yn26ITopm9GRQc04KhERERERERERERERaYlW7Evi4W93kppdWKmta5AHb14zkD6hPtX20S3Yk4V3jeLRBbtYtOO4TduGQ2lMmbOWt2cMYmhn/1qNzWKx8vbKGF5bfhBrhcJP/h7OvH3dIEZ0DahVn/b4uDvx5wGhpdMLbo5LP111Kom4k7m17i+roJhtRzPYdjTDZruD2WS3gtWFUUG8MX0Afh7OdX0KIiIiIg1CYSlptboFe9mGpSp8kkNERERERERERERERNq2vMIS/vHjXj7bcNRu+w0jwnl0Uk/cnKue2q48DxdH3rxmAEM6+/Hc4r0UlZSFgpKzCrjm/Q08OqkHt5wfUaMqUNkFxTw4fwc/7zlRqa13B2/e+8vg0ikBG5KTg5kRXQMY0TWAJy/tRdKpfA4mZXEwKZvopCwOJmURnZRdqcpVTVQMSplMcP/4KGaP7YbZTtUuERERkaamsJS0WpEhnjbrMclZzTQSERERERERERERERFpaXYlZHLvV9s4lJJTqS3Q04WXr+rH2B7Bte7XZDJxw4jO9A314c7Pt5KYmV/aVmKx8vySfWw5ks5LV/XDy9Wpyn6OnMzhtk82czCp8ofB/zygAy9e0a/GIa76CvF2JcTb1WYGD6vVyolT+TYBqoNJ2cQkZ5NdwxCVn7sTb14zkAuiNDOIiIiItBzm5h6ASF1FBtuGpQ4mZWOtWJ9WREREREREWLVqFSaTiZkzZ5Zui4gwPuleflm1apXd4xcuXMjVV19Np06dcHV1xdfXlyFDhvDss8+Snp5e7bkPHjzI3XffTZ8+ffDy8sLZ2ZkOHTowYMAAbr75Zr766isKCgpK9+/cubPNJ/CfffbZSuO86aab6vX1qEp2djYvvvgiI0aMwN/fHxcXFzp27MhVV13F4sWLz3r8li1buOWWW4iKisLDwwNXV1fCwsIYPHgwd911F4sWLbL7d2t+fj5z5sxhzJgxBAUF4eTkhL+/P927d2fSpEm89tprxMXFNcIzFhERETk3lZye1u7yd9bbDUqN7xnC0vtG1ykoVd7ATn4svvt8RkcGVmr7afcJ/jx3PQdO2P+g9+qDKUx9a12loJTZBI9P7skb0wc0WVCqKiaTifY+blwYFcSto7vw0lX9WXjXKHY9czG/PXIR82YO5fHJPbl6cEf6h/niUWG8A8J8WXzPaAWlREREpMVRZSlptSKDvWzWM/OKSM0uJMjLpZlGJCIiIiIicm5JT0/nqquu4tdff7XZXlBQwJYtW9iyZQvvvPMO33//Peedd16l47/++muuv/56CgsLbbYnJiaSmJjIjh07+Oijj9i1axd9+vRp1OdyNtu2bePSSy/l+PHjNtuPHTvGt99+y7fffssVV1zB559/jqura6XjX3/9dR588EEsFovN9oSEBBISEti6dSvvvPMOWVlZeHqWffgnMTGR8ePHs3fvXpvj0tPTSU9P5+DBg/z8888cP36cV155pQGfsYiIiMi5KT4tl/+bv51NcZVD/W5ODjw1tRfXDA2r0RR5NRHg6cK8mcN4c0U0c1ZE27QdSs3hsrfX88IVfbh8YEfAqNb0/ppD/Ovn/VSYrQ4fNyfmXjfQprpTS2Qymejg60YHXzfGdC8LnFksVo5n5hGTnI2zg5nhXQJw0LR7IiLS0HJSoTgffDo290ikFVNYSlqtUD83XJ3M5BeV3YiOTs5SWEpERERERKSCoUOHsmvXLr7//nueeOIJAJYuXUqHDh1s9ouIiCh9XFBQwPjx49m6dSsODg5cd911TJ48mYiICIqKilizZg2vvfYaycnJTJ48mW3bthEeHl56fFJSEjNnzqSwsJDg4GBmz57NeeedR2BgIHl5ecTExLB69WoWLlxoM4Zly5ZRWFhI3759Abjjjju48847bfbx8/NryC8Px44dY9y4caSnp5dWrrrmmmsICAhg7969vPrqq+zYsYMFCxZw00038eWXX9ocv3PnztKgVEREBLNnz2bAgAH4+/uTlZXFgQMHWLlyJd9//32lc999992lQanrr7+eK664gg4dOuDg4EBiYiKbN2+2e5yIiIiIGKxWK0mnCjiYlMWuY5n8e1UsWXamiOvf0YfXpw+gS5CnnV7qx8Fs4v8mRDGwky/3f7WdjNyi0ra8ohLu/2oHW46k87eLe/DE97v5YcfxSn30aOfF+38ZQqcA9wYfX1Mxm0109HOno1/rfQ4iItLCbfwP/PQQWC3Q92q49A1wafj/2+Xcp7CUtHwlRZAZD+lHwKsdBPcEjD8+ugZ5suf4qdJdY5KzGdm1crlbERERERFpxSwWyEtr7lE0LTd/MJsbrDsPDw/69OnD5s2bS7dFRUXRuXPnKo/5+9//ztatW/H19WX58uUMHjzYpv38889nxowZjBgxgsTERB577DE+//zz0vYlS5aQk2NMebJixYpKlaNGjhzJDTfcwNy5c222R0VF2awHBwc3etWp++67r3Q6wf/85z/ccsstpW2DBw9m2rRpTJo0iZUrV/LVV19x4403MmnSpNJ9vvnmGywWCx4eHvz++++EhITY9D969GhuvfVWMjMzcXcve+MoPz+fRYsWAfDAAw/YrRw1depUnn32WdLS2tjPgIiIiEgFVquV5KwCopOyOZiURXRyFgdPP87KrxyOOsNsgtlju3H3uEicHBrud2x7xnYP5ofZ53Pn51vZdSzTpu2zDUf5enMCBcWWSsdN7tuOl6/qj4eL3rYTERGpUvymsqAUwK6vIWkvXPM5+EdUf6xIBfqtS1q2JQ/C5v+WXfBGzIZL/lHaHBlsG5aKrjC3t4iIiIiInAPy0uDlrs09iqb1t1jwaL4PgmRnZ/P2228D8Nxzz1UKSp0RHh7Ok08+yZ133snXX3/N+++/j4eHBwAnTpwAjCpQ1YWd3NzcGnj0tXP8+HG+++47ACZOnGgTlDrDxcWFDz/8kMjISIqLi5k7d65NWOrMc42KiqoUlCrPx8fHZj0tLY2iIqPqwAUXXFDtOP39/Wv2hERERERaOavVSkp2WSjqYFI20UlZRCdnk5lXdPYOygnzd+ON6QMYHN50v0uF+bvz9e0j+Pvivfzvj6M2bRWDUiYTPHhxd+4c07XBpgUUEZFW4lQibP0YLMXQaYSxOKsyX5UKc2Hh7WW5gTOS98D7Y+Dqj6DrRc0yNGmdFJaSls3Fy/aCl3HEpjkyxMtmPTo5qylGJSIiIiIick5bvXo1mZnGJ+Gvuuqqavc9E/IpKipiy5Ytpevt27cHID09ne+//54///nPjTjiulu1ahUlJSUAdoNSZ3Tu3JkJEybw008/lR7j4OAAlD3XvXv3snHjRoYNG1ajcwcEBODs7ExhYSGffvopkydPxtFRt2pERESkbSgusZCYmc/RtFxikk9Xi0rK5mByls00dnV11eCOPD21F16uTg0w2tpxdXLghcv7MriTH48v3EV+UeVqUl6ujsy5ZiBjewQ3+fhERKQZWa2w4wv46REoKFeF0MEFOg2HLmOh61ho179Bq463er8+Bydj7LflZ8BnV8KEvxvFVxRAlhrQHThp2fw6266nx9msdgu2nX80JlmVpUREREREROqr/HR9Z4JANXGmwhLAn/70J3x9fcnIyODyyy9nzJgxTJ06lQsuuIABAwaUBo2a2+7du0sfDx8+vNp9hw8fzk8//URubi6HDh0iMjISgGuvvZZ//vOfFBQUMGrUKCZOnMiUKVM4//zz6d27d5VVAlxcXJg+fTqffvop33zzDZs2bWLatGmMGTOGkSNH4uvr22DPU0RERKQ5ZBcUc/RkLkfTcjialsuRk7kcTcslPi2XhPQ8ii3WBjuXh7MD3UK86B7iyZ/6h3J+ZPNVaj3jysEd6R3qzR2fbeVwak7p9m7Bnrz/l8F0CfKs5mgRETnnnEqExffBwZ8rt5UUwOE1xrLiWXDzg4gLjeBUlzGV3zdvS+LWwYZ3K2w0AeV+j7BaYNkTkLgDps5RlS45K4WlpGWrFJY6YqRtT99ojqwQlkrNLiQtpxB/D+cmGqCIiIiIiMi5Jzk5uU7H5ebmlj4OCAhg0aJFXHvttRw7doyVK1eycuVKALy9vRk3bhw333wzl156aYOMua7S0tJKHwcHV/+p/nbt2tk9rkePHnzxxRfcdtttpKens3jxYhYvXgxAYGAgEydOZNasWYwePbpSn3PnziUjI4MffviBI0eO8PLLL/Pyyy9jNpsZNGgQ06ZNY9asWZWm8BMRERFpCSwWK0lZ+aUhqKNn/j29pOUUNvg53ZwciAzxJDLYi6gQT6JCvIgM8STU161FTmXXo503388exZzl0ayLSeX8boHcOz6yWSpeiYgAkJsGC26D49uhx2QY/yy4a+r3RmW1ws758NNDRhWkmshLh70LjQXAL+J0cGosRIw2wlRtQUE2LLwTm2CUoyvM/AnWvgr7F9vuv+trSDkA13wOvp2adKjSuigsJS2bX7jtesEp4z+G0/9hd/J3x9nBTGFJWQnbmORshkXoP3QRERERkXOGmz/8Lba5R9G03Jr3b5oz09IBbN26FSenmr2R07FjR5v10aNHExMTw7fffsuPP/7ImjVrSEhI4NSpU3z33Xd89913XHLJJSxYsAB39+b/xF993ly78sorGT9+PF999RVLly5l7dq1pKSkkJqaymeffcZnn33GjTfeyIcffoi5XBl9b29vFi1axMaNG5k/fz6rVq1i+/btlJSUsHnzZjZv3swrr7zCwoULGTFiREM8TREREZE6i03J5qddiWw5ks6RtFwS0vJs7s83JFcnM5HBXpWCUaG+bpjNLS8UVR1vVyeeuLRXcw9DRMSw8A6IWW483voJHPgZpr4BPaY067DOWVlJsPh+OLDEfntQD0iNBmuJ/fYz0g/D5sOw+UMwmaHDwLIp+zoOA8dztJjIL09CxhHbbeOegtBBMO1TIzC18nnb9hM74f0xcPXHRrBMxA6FpaRl8+4IJgfb/xzSD5eGpRwdzHQJ8mD/iazS5ujkLIWlRERERETOJWYzeDT/FBptSUBAQOnjoKCgSiGo2nB1dWXGjBnMmDEDgMOHD7NkyRLeeustDh48yNKlS3n88cd5/fXX6z3uuvD3L/v7MSkpibCwsCr3LT/NYPnjzvDx8WHWrFnMmjULgH379vH999/z1ltvcfz4cT7++GMGDhzIvffeW+nYYcOGMWzYMACysrJYtWoV8+bNY8GCBSQnJ3PllVcSGxuLm5tbnZ+riIiISF0cTs1hyc7jLN6ZaHMvvqF4ODvQOdCjtEJUVLAXUSFedPRrfaEoEZEWL2ZF5SngcpLhy+ug33SY+KKqTDUUqxV2fws/PmgUA6nIIximvmlU98rPhMNr4dAqOLQSTsacpW8LHNtiLGtfASd3CB9VVnkquGfpTE2tWswKIxxWXqeRMPwO47HZDBf+Ddr1gW9vg8Jyv6fknoRP/my8pofddm58PaRBKSwlLZuDI/iGQXpc2bb0OAgdXLraLdjTNiyVlN104xMREREREWlFalo5aeDAgaWP169fz/Tp0xtsDBEREcyePZsbbriB3r17k5CQwPz585stLNWnT5/Sx3/88Ue1YamNGzcC4O7uTpcuXc7ad8+ePenZsyczZsygZ8+e5OTkMH/+fLthqfK8vLyYOnUqU6dO5d5772XOnDkkJiaybt06JkyYUMNnJiIiIlJ3cak5LNmVyJKdiexNPFWvvkwmaOftSid/97IloOyxv4dzi5w+T0TknFNSDEsfq7p951dwaLUR4Ok+senGdS7KToEl98O+H+y3970aJr1UFkxz9YGelxoLQEa8EZo6tMpYck9Wf76iXIj5xVgAPEOgyxgjONVlDHi3r/9zamp5GbDobtttTh5w2dtGSKq87pPgtl/hy2ttg2bWEvjpb5C4A6a8Ck6ujT5saT0UlpKWz69z5bBUOZHBXkBi6XpMssJSIiIiIiIi9ri6lt0UKigoqHK/8ePH4+7uTm5uLnPmzGHatGkN/gaWt7c3Q4cOJSEhgdTUVLtjzc/Pr3acDWHMmDE4ODhQUlLChx9+yFVXXWV3v6NHj/LLL7/YHFNTYWFhREVFsW3bNrvPtTrjxo1jzpw5ALU+VkRERKQ2jp7MNQJSu46z+1jtAlJuTg508ncnzN+d8ADbUFSorxuuTjX/3UlERBrJlo8gZX/1+2SfgC+mQ//rYOI/wc23SYZ2TtnzHSx5wH7AyT0QLn0dev2p+j58w2DQDcZisUDSLog9HZ46+jsU51d/fHaSEX7b+ZWxHtSjbMq+8JHg4lWnp9akfn4UTh2z3Xbx38G/ig+vBUUZgalvb4PopbZt2z+DlH0w/TPw7tA445VWR2Epafn8Otuup9vOSRoZ4mmzHp3c8GWARUREREREzgXt25d9kjA2Npbu3bvb3c/X15fZs2fz0ksv8dtvv3H//ffz2muvYa74yb3TkpKS+OGHH7j11ltLty1dupR+/frZnLO8zMzM0kpNERERdsd6+PBhYmNja/z86qJDhw5cfvnlfPPNN/z00098/PHH3HjjjTb7FBYWcvPNN1NUVATA7NmzbdoXLlzImDFj8PX1tXuO+Ph49u83bkiXf66HDh0iPj6eCy+8sMrxLVu2rPSxva+TiIiISH3Ep+Xy465EluxKZGdCZo2OCfJyYVKfdgwI8yU8wAhIBXm6qDqUiEhLlpsGK/9hu61dX2M6s58fhYIK/wfs+J8RzPnTHIhUheMayUk1QlJ7F9pv7305TH4FPAJr16/ZDO37G8v590FRHhzdUDZlX+JOwFp9Hyn7jeWPd8HsCB2HnZ6ybwx0GGTM9tSS7P/ReA2W12UsDLml+uNcfeDaL2HVC7DmZdu2Y1vgvQth+qfQ6byGHa+0Si3sVS9iR6WwVJzNamSwbVgq6VQBp/KL8HZ1atxxiYiIiIiItDIDBw4srdj05JNP4uTkRHh4eGkIKjQ0FDc3NwD+/ve/s3r1av744w/efPNNVq1axW233caAAQPw8PAgPT2dPXv2sHz5cn766Sf69u1rE5b64osvmDp1KhMmTODiiy+mT58++Pv7k5WVxe7du5k7dy7HjhmfELz99tsrjXXkyJEcPnyYRYsW8d577zFq1KjSylje3t4EBwc32Nfl9ddfZ8WKFaSnp3PzzTezbt06pk+fjp+fH/v37+eVV15h+/btAEybNo1JkybZHP/GG28wY8YMpkyZwkUXXUTPnj3x8fEhPT2dzZs389Zbb5GXl1fpuR49epSxY8fSq1cvLr/8coYMGUJoaChgBKy++uor5s+fD8CAAQMYPnx4gz1nERERabsS0k8HpHYmsqOGAalATxcm923HlL7tGdLZHwezglEiIq3K6n9BXrrtton/gs6jjNDMonvKpnA7I+s4fH4VDPwLXPIPI4gi9u1dBIvvh1w7FaHdA4wp4Hpf3jDncnIzvmddxwLPQs5JOLzqdOWp1ZB5tPrjLcVw9DdjWfkPcPGBiNFl0/YFdDXm0G0uuWnww72221y84c9zazYusxkuesIIA353BxTllLXlJMO8S2HyyzBkZsOOW1odhaWk5TtLWCo8wAMHs4kSS1liNiY5m0Gd/Bp/bCIiIiIiIq2Il5cX99xzDy+99BJbt27l4osvtmlfuXIlY8aMAcDFxYVffvmFm266iQULFrBjx45KFZXK8/b2rrStqKiIH3/8kR9//LHK426//XbuueeeStsffPBBvvnmGwoKCiqFqW688UbmzZtXzTOtnY4dO7JixQouvfRSjh8/zgcffMAHH3xQab8rrriCjz/+2G4fubm5fP3113z99dd2281mM88++yyXXXZZpba9e/eyd+/eKsfXo0cPFixYoGoNIiIiUmfHMvL4aVcii3cmsj0+o0bHBHo6M7FPO6b07cCwCAWkRERarZQDsPE/ttt6/dkISoExLdmMr2HbZ7D0MSioMBXrtk+NIM6f34KuFzXNmFuL3DT48W+w+xv77T2nwpTXwTOo8cbgEQB9rjQWqxXSDhkVp2JXwuG1lauGVVSQCfsXGwuATxh0udAITnUZa/TflJY8YISaypv4Ivh0rF0/vf4MAd3gy+ts8wWWIlh8HyTugEkvgaNzfUcsrZTCUtLy+YbbrmcmQEkROBiVo5wdzXQOcCc2pSwVGpOksJSIiIiIiIg9L774IpGRkXzyySfs2bOHzMxMSkpK7O7r5eXFt99+y7p16/j4449Zu3Ytx48fJy8vD29vb7p27cqwYcOYMmVKpeDV66+/zoQJE/j111/ZuXMniYmJpKSk4ODgQFhYGCNGjODWW2/l/PPPt3vuAQMG8Pvvv/Pyyy+zfv16kpKSKCgoaPCvxxkDBw7kwIEDzJ07l4ULF3LgwAFyc3MJDAzkvPPO46abbmLq1Kl2j/3iiy9YvHgxq1atYu/evZw4cYLU1FRcXV0JDw/nggsu4Pbbb6dfv342x40ePZpVq1axdOlSNmzYQHx8PElJSeTn5+Pv70///v254ooruOmmm3BxcWm05y4iIiLnFqvVyqHUHDbHpbEpLp3NcWnEncyt0bH+HkZA6tK+7RkW4Y+jg/1pmEVEpBVZ+jhYy/3d7+ACE56z3cdkgkF/MaoLLbrbCNuUdyoBPr0cBt8EFz8PLl6NPeqWb/8S+OG+ysEeADc/Y8q9Plc2bZUmk8moDBXQFYbeCiXFkLj9dNWplRC/0QgLVScz3gjObfsMzE4w6l4Y+xiYHRp//LsXwJ4FttuiJsKA6+rWX0hvuG0lfHNz5df0lo8geR9M+wS8QurWv7RqJqvVepYJLKW1S0hIICwsDDDK+HfsWMvUZXPLTYOXImy33bMd/Mu23f7pFn7ec6J0/bbRETw+pVcTDVBEREREROoqOjqa4uJiHB0diYyMbO7hiLRZdflZbPX3G6TB6TUhItI8ikos7D6Wyea4dDbFpbH5SDppOYU1Pt7P3am0gtR5XRSQEhE5p0T/YkylV97oB2DcU1UfY7XClnmw7AkozK7c7tPJqDLVZUxDjrT1yE2Dnx+BnV/Zb+8+BS59vWUGcAqy4chvZZWnUvbV7Liu4+Cq/xohsMaSnQxvD4e8tLJtrr5w1x/g1a5+fZcUw4pn4bc5ldu8OsD0z6Dj4PqdQxpVY9xvUGUpafnc/Iy5UsuXCEyPswlLRYZ48vOesuboZDv/cYuIiIiIiIiISJM4eRLqUhDN0xPc3Oy3paYa79vUhbs7eHjYb0tLgyoK7J2Vqyt4VfGh+owMKDrLh7ar4uwMPj722zIzobDmGQgbTk7g62u/LSsL8vPr1q+DA/j722/LyYHcmhW0qcRkgsBA+215eZBdj1uAQVXMhFJQAKdO2W+riYAAMNvJmRQWGt+7uvLzA0c7d/OLiyE9ve79+vgYr7eKLBbj57iuvL2rvgakpNS9X10jDAWWIg5lZpRWjtoWn05+kQUAS4Ej1hIzUP2UMj5ujlzUPZgJvUMYHO6Pk4MZJydwrKJohK4RBl0jDLpGlGmJ1wj9HmHQNQJjpp5vXsLP4oCj+fQL1TMEzr8fqO4aYYLwmTBtAvz0CBz9zbY5JwfevRmfUVfhPOkpcPG0aT6nrxExK4xKXTnJgDE9nbtTHh7OuUawZ9JL0G9aaTWplneN8MQ55GJ8ok5XB886AYdWna48tYrM1FwKS5wqH7ZzOxy9Ai5/D4Ki7PZcr2uE1QrfPQGpJs58XQEY+wYORe2o4hJRi2uEIwx8DlyGws+PQnEeJpOVQPc0yDoOH02CKa/CwOvBZGo514iUg8b3p9CYYSvAO9f+NaLITGaOa+0G2fdK8O0EtI7fI+pzTamSVc558fHxVsAKWOPj45t7OHXz7vlW69PeZcumD22aF25LsIY/vLh0GfnPFc00UBERERERqY2DBw9a9+7daz148GBzD0WkTavLz+I5cb9BGlT51wTEW4073rVb5s6tuv/AwNr3d2Z5+umq++3Vq+793nln1f1eeGHd+73qqqr7veqquvd74YVV93vnnXXvt1evqvt9+um69xsYWHW/c+fWvd/q7orPn1+/fpOT7fe7cmX9+t29236/u3fXr9+VK+33m5xcv37nz6/6a1yffnWNMBb37sdt7oeXX9y7H69zv7pGlC1V0TXCoGtEmZZ4jdDvEcaia0TZsvuOYWXvsW79rLTfel8jbpxstb7e12o9tMZmvG3uGnHhC1br59Os1szjlfptVdcIi8V61dTMOvfb6q4R7im2+YOnva3WDyZYrXG/tdxrxIMRlcf8tLd15Y2T69Vv6/g9ouHvQamWqrQOfp1t1zOO2KxGBtvGao9l5JFTUNzIgxIRERERERERERERERERaQU6DIT+1zZsnxlH4ONL4ceHSqvftDk9LoVrvwTv9s09kvoxmcDFu7lH0XTMdipoxf8BH02ETR82/XikyWkaPmkdKoal0uNsVrsEeWA2gcVati02JZt+HX0be2QiIiIiIiIiIiIiIs0uMtgTN3939jf3QESkeVmtsO4N2PElOLqAX4TxPpt/BORMBoKbeYDSbCa+aH+ev4aw8T3Yt8gIZJl6A080znlaonZ9wdTcg5Bac/WG4XfAH+9Wbkve0/TjkSansJS0DmcJS7k6OdDJ3524k2WTkkYnKSwlIiIiIiIiIiIiIs2vsNjCwaQsdiZksutYJruOhQG+de7PycFEv46+DOnsx9BwfwaH++Hn4czVv6OwlEhbF78Blj9Ttp64o+zxEV/gsqYdj7QMfa6ETuc17jmyEuFAIuT8QZsKS0krZYJJLxo/F788VWlmqxapxxTwzrLX0ORDOReYrFar9ey7SWuWkJBAWFgYAPHx8XTs2LGZR1QHMcvhsyvL1t384OE4m11u/Xgzy/clla7ffmFXHpmkC4OIiIiISEsWHR1NcXExjo6OREZGNvdwRNqsuvwsnhP3G6RBlX9NbN8eT4cOtX9NeHqCm5v9ttRUo1BCXbi7g4eH/ba0NCgpqVu/rq7g5WW/LSMDiorq1q+zM/j42G/LzITCwrr16+QEvr7227KyID+/bv06OIC/v/22nBzIzbXfdjYmEwQG2m/Ly4Ps7Lr1CxAUZH97QQGcOlX3fgMC7BdsKCw0vnd15ecHjnY++lxcDOnpde/Xx8d4vVVkscDJk3Xv19sbXFzst6Wk1L3f1nKNKCqxEJuSTUxqJjEZGew6lsn+xCwKSyyl+1jyHbFaal7dw9PFgf4dfRnQyZdhXfwY2csbVyeHSvvpGmHQNcKga0SZlnSNOKPBf49IjYHv/orzqYP4uNp/oWbme1NYYmfqqYrc/ME3DHw6gW8n8A3DKbgLvr0HgrnytUfXCEOLuEZYrTD/RjiyrnSTn2c+jvf+YXxPy6nXNcJiwefgf3Be/TQU59k2WU2czK3im2qPs6fxWvMLA58wvEPb49IuHDoOAxdPm10b/RpRkA2rXjAqs9kTfj5M/Cf4hJZuajXXiNPq9LeG1QpbPoaV/wBrhSfk7AVTX8epx0W1+z0i4yh8NBmKyv2Qe4bAzJ/ArayjJrlGFBfClo9g9UuQm0pekSvZhRW+qc5eMHwWDL4JnKv4hp921muExQL7F8O614yvgz0OzjDwL0b1Kw/jC9CWf484fjyBAQMa9h6UwlJtwDlx8/JkLLw1yHbbw0dsLpT/+nk/766KLV0f3zOYD24c2kQDFBERERGRulBYSqRlUFhKGoJeEyLS1mQXFHP0ZC5H03KJT8vl8Mkc9hw/xb7EUxQWW87eQRVMJugS6EHfUB8Gh/sxpLM/USFeOJg1x4+IVGH/j7BgFhTaqzjSgAK6wUVPQq8/GxcraXkO/AxfTLfddsFDcNHjjXO+1Bj4/i6jollDc/aEvlfDkJnQvn/D91/RoVXw/d2QaSe84uwJFz9vBGXa8mv/8BojjJeXVqHBZLzGRj9Ys6+PxQIfT7UJ9QEw41uIHN9gw621giz4bS789hYU5djfx7MdjHnYCDI51CB8Wp7VCrG/GtX/TuysYicT9L8Wxj5qhFUFaJz7DZqGT1oHnzCMyV7LZfsybMNSkcG2yeLo5HpEwkVERERERERERESkTbNYrCRl5XP0ZC5HTgeijqblcuSk8fhkTh1LOFXQJdCDvh196BtqLL1DffB00ds3IlIDFgusedmohFOReyCMuhdykiE9zljS4uoXqDoZA1/fCB0GwYRnIeKCuvclDa+4EJY+ZrvNqwOcf1/jnTOwG9z8MxzfBsl7Ie1w2est/TDk1qMMXWG2Ue1ny0cQOhgGz4Q+V5y1qk+tFWQb07Bt/q/99ogL4M9vK7gCxtdi1ir4agac2FWuwQq/Pg+JO+GydytVBKtk4/uVg1KDbmzeoBSAi5cRUhp6i3Ft3fwRWCqU8Mo+AYvvh9/frl149NgWIyR1eE3V+0RNhHFPQUjvej0NqRn9ti2tg6Mz+HSEzPiybelHbFLEkcG2dQiPpuWSX1RitxSxiIiIiIiIiIiIiEheYQlHT4egjqblcvRkTunj+PS8elWIsici0IM+oT70C/WhT6gPvUO98XatZVUCEREwKqB8d7sxlVNF7fvD9M8rTbuG1Qq5aWVBlvTDZSGq9Dg4dQybwgVVOb7VqArTdRyMfwba96vvs2kZjvwOu78BF28jFNJpBDi5Nveoam7j+5AWa7tt/DMNHy6qyGSC0EHGUlH+qXLhqbiy11x6nDH9mKW4Zuc4tsVYlj4G/aYb1aYaIlByeK1RGSvjSOU2Jw+4+O8w+Gb7c5+1VX7hcPMyWHS38fNS3r5FRqjyms/Bv4v941NjjNBQeT6d4JJ/NMpw68QzGCa/DOfdAb/+o/LzhJqHR0/Gwoq/w96FVZ+v4zCjj/CRDTJ8qRmFpaT18OtcISwVZ9PcNdj2P3qrFWJTsundoYpJV0VERERERERERESkzckvKmHhtmN8uuEIe46farTzhAe4l1aL6tvRh94dfPBxUzBKRBrAyVj48jpI2V+5rd90mPomOLlVbjOZwCPAWDoOrtxeXGAEWEorUZ0OtiTvNUIuFcWuMJa+04wpuPw61+95NZekPbD8WYheWrZt3Wvg6GoEprqOhS5jIKRvyw3N5KTC6pdst4UONqaxa06u3kaYzl6grqTYCOiVD1ClnQ7xndgFVjuB5YJTsOk/xtJxmBGa6n25/dd7dQpzjO/5xvfst3ceDX+e23pf043N2R2u/MAIZi5/2vZ7lbwX3h8LV30I3cbZHmcpgYW3Q3Ge7fbL3jaqOrU0/l3gqv/CqHuM10vsisr7VBUezToBq/8FWz4Ga4n9/gO7w/inofvktj29YzNRWEpaD79wiFtbtl4hLOXu7EhHPzcS0ssurjHJCkuJiIiIiIiIiIiICCSdyufT34/w+R9HSM8tOvsBNWAyQTtvV8L83Qn3d6dLkCf9OvrQp4MPPu4KRomc86zWpn+DO/oX+OYWKMi03W4yw8XPw3l31n1Mji4QGGks5VmtRlWUFX+HtEOVj9s1H/Z8B0Nuhgv+Bp5BdTt/U8s4CitfgB1fYreiVnE+HFppLGBMbdjlQiM41WVs5cpdzWnlPyq/Jib+q+WGuwAcHI33f/3CK7dlxMO2T2HrJ5CVaP/4hI3G8vMj0P9aY5q+4B5nP++R32DhnfYDgE7uMP5ZGHpry/7atQQmkxEiCukN39wM+RllbfkZ8PlVxtdy5N1l16Tf5kDCJtt+hv215U/p2b4//GUBHFpthMOOb6u8T2l49GrwCYM//g1Fufb78w6FMY8ar1sHRXaai77y0nr4drZdrxCWAogM9rQJS0UnZTfumEREREREpF4cHBwoLi6mpKQEq9WKSZ+iEmlyVquVkhLjU45m3QwWEZFz0Pb4DD5af5glOxMpttRgeqkK3Jwc6OTvTpi/O5383QkPcC9d7+jnhquTQyOMWkRaBKsVclIqV1o6M51YTqoRLBowAwZcB+7+jTuWda8bgaWKwR43P7jqI6MCUmMwmYzqPT0uNcIrq/8F2Um2+1iKjCo92z83whEj7mqZlWIAck7C2leN6kQlhTU/LjcVdn9rLAAB3cqCUxGjwbWZCjic2A1b5tlu6zsNwoY2y3AahG8YjH0MLnjIqPi1+SOIWY7dUFt+phFM+ePfRiWwwTOh158rT6FYmAu/Pgcb3rXfT6eRRoWjqqaPE/u6jYNZK+GL6yBlX9l2qwV+eRISd8Cf3jKumytfsD3Wv4tRWam16HIh3LbydHj0ucrTXgLs+rrq4119YfT/wbBZta+GJg1OYSlpPSqWObQXlgrxYuWBlNL16OSsxh2TiIiIiIjUi7OzMwUFBVitVnJzc/Hw8Dj7QSLSoM78DILxMykiInIuKC6x8POeE3y47jBbj2acdf9gLxc6+bvT6XQQ6kwoKszfnSBPF4X6Rc5lxQVGFZszAaiKwaiinOqPT94Lyx43Qky9/mxMC9ZpRMNWnCrMge/vMqo3VRTSB675vGmmC3NwgqG3QP9rYMM7sO5NKKzwXlxhNqz6J2z8D1z4MAy+CRxbyN8ZhTnGuNfPMaZzs8cjyKgKk7jd/jRw5Z2MMZZNHxiVvUIHG8GprmMhdEjTPG+rFZY+ajtWJ3djOrBzgYMj9JhiLOlHjLDetk8rh/XOOPq7sfz8sBFiHHyTEWg8+gcsvMN+uMXRzQjsDPurqknVlX8XuPUX42u87wfbtt3fQOoBI59WPpxoMsNl/wbnVnYvsHx4dNunsOpfkH2i+mMcXWH47XD+fUa4VVoEhaWk9aj4S17GUWNeU3PZp3a6BXva7BKdrMpSIiIiIiItmbe3N1lZxo3VtLQ03N3d9UaUSBM7darsTQIFFkVEpLXLyC3ki43xfPJ7HImZ+dXue0FUEDNHdua8LgG4Oas6lMg5r6QIYlfCiZ2nQ1FHjDBUZgJ2q8zUuv8CYzq6XfMhqIcR0uh/Tf3fGE+Pgy9nQNLuym29LoPL3mn6sIGzhzHd3uCbq67QlJsKP/0NNrwNFz0Jva9oviBKSVHVFbHOcPaEkfecrojlCXnpcHitMQVf7Er7U7aVZ7UY04slbII1Lxn9hY8yglO9rwCvkIZ/XgAHfoTDa2y3jboPfEIb53zNyS8cxj0JYx6BAz/B5g/LpkisKC8dfp9rLO0HGNWN7P2chw2HP78Dgd0ac+Rtg4sXXP2JcU1Y+Q9svt4ndlXef8Rs6DS8yYbX4BycjKlH+003qpWtf7NyCNNkhoF/MV6z3h2aZ5xSJZP1zEcH5ZyVkJBAWJgxZ258fDwdO3Zs5hHVUXYKvFLhP6r7dtvMB7ztaDqXv/Nb6bqD2cTev1+Ci6P+0BURERERaYksFgsHDx4srWrj6emJv7+/QlMiTaCkpISMjAySk5NLt3Xt2rXG1aXOmfsN0mD0mhCR5hSdlMVHv8WxYGsC+UVVVwJxdTJz5aCOzBzVmW7BLXR6KBFpWOlHYOvHsO2zqoMyjcXR1ahAMngmhA2rfbWpQ6vg65uM4IcNE4x7Cs6/v2ErWNVVxlFjeq0dX1Jl8KxdP6N6T9dxTTdmq9WoxvXr8/YrCgGYT1fLGv0geAZV3Vd6nPH9iF0Jh1fb+Z5Uw8kdzrsDRt3bsFP1FRfA28Ntg1zeHWH2JnB2b7jztGRph2DLx8b0jzkpZ9//DAcX42fovDtsCnNIAznwEyyYVXUFt6AeMGt15akSW7PcNCMotvlDKM43qqFd9BQERTX3yM4JjXG/QWGpNuCcuVFltcILobblTm9aAp3PL13Nyi+i7zPLbA5bet8FdG+nP3pFRERERFqqrKwsjh07Rvk/T00mEw4Oulkl0lisVislJSU224KCgggMDKxxH+fM/QZpMHpNiEhTs1isrI5O4cN1h1kbnVrtvu19XLlhRGeuHRaGr3sLmQ5KRBpPSTFELzXetI5ZQb0rR5mdjKo2fp1PLxHGv46usPNL2Pt95cpKFQX3Pl1tavrZAzNWqzFd3LInKk8F5+IDV/0XIifU/fk0lqQ9sPxZ42tflYgLjCniQgc37lgOrYLlz8DxbVXsYIJ+02DsY7WfwtBigRM7jODUoVVwdINRWexs3PyMUNbQWxsmJLL+TfjlKdttV/4X+l5V/75bm+JC2L8YtnxUudJWRaFD4LJ3FWJpbCkH4cvr4GS07XaTA9y2AjoMbJ5xNbbiAmNx9W7ukZxTFJaSOjmnblS9MxKS95St//ltGHi9zS4j/rnCprzy3OsGcmk/lbUTEREREWnJ7AWmRKTp+Pj40L59+1pVdDun7jdIg9BrQkSaSk5BMQu2JvDRb3EcSsmpdt9BnXy5+fwILundDieHZpr+SUSaTmaCMd3a1k8h63jtjnXzN0Iz/hGVQ1HeHaqvPpNz0qhss2Ve1RWMznB0gz5XwpCZRmCo4u/gRXnww72w86vKxwZ2h2u/gICutXpqTe7Ib/DL05Cwsep9Ii4wqsv4lf96d65/RaTEHUZIKvbXqvfpNsGoctWub/3OdUZhLhz93QhOHVppf8qx8nzCjJBWv+l1r2qUnQxzBkFhVtm2sOFw89KWUW2sOZ2MNUJT2/8HuSfLtjs4w9jHYeTdqibVVPIzjQpTB38u23bhIzD20eYbk7RKjXG/wbHePYg0Jb/OtmGp9LhKu3QL9rQJS0UnZTf+uEREREREpF68vLyIiooiOzubU6dOUVhYWKnqjYg0LAcHB9zd3fH19cXV9RwqfS8iIq2exWIlq6CYzNwiMvNsl5jkbL7ZEs+p/OIqj3c0m5jSrz0zR0UwIMy36QYuIs3DUgLRvxjhiOhllSsxVdRhELTvXyEU1bl+06N5BMCoe4wQxuE1xlj2LQZLUeV9i/Ng+2fG0q6vMUVf36uNKiQZ8fDVDCPwU1H3KXD5v1tHtZLwkXDLMti/BFb8HVIPVN7n8Br7FYA8Q2wDVOW/T54hVQeB0g4Z0+3t/rbqcYUOhvHPQsTo2j+n6ji7Q7dxxgKQnWJM1XfwZ2M8FV+TmfGw8A747S0Y9zREXVL7gNOvz9sGpQAm/lNBKTDChBc/Dxc9Cft+MF6HTu7Gz2dwj+YeXdvi6gPXfAFb50H0cmPGqPPuaO5RiQAKS0lr4xduu24nLBUZ7GVTcjkmWWEpEREREZHWwGw24+3tjbd3K7jxKyIiIiI1UlhsITo5i8zcIjLyKoefMvOKKoWiTuUXUZeCo37uTswYHs7154XTzkdBYJFmYbFAViJYS8CrAzg04luRp44bFaS2fgKnEqrf18XHmP5u8E0Q0rvxxmQyQZcLjSU7xQhEbZln9/0swKhAtOT/YNmT0PsyOLgUcu1MKzrmUbjgITC3ogp5JhP0vBSiJsKO/8HKf9as2ld2krHEb6jc5uhWOUTlGw4xy42AmqWKIG1ANyOU1HNq04SJPIOMqfD6XmV83379uxHaqSh5L3wxHTqNMEJcnYbXrP/Encbrvrz+1zb+1IatjaNL2fdBmo/ZDENuNhaRFkRhKWldKs4ZbC8sFeJpsx6dnFVpHxERERERERERERFpPFarlc//OMoryw6QkWunskoD6h7ixcxRnblsYCiuTppWR6TRFeYa78+kx0H64bLHaYch4wiUFBr7mRzAN8x2OrvyIZe6VHKylBjTq23+yKjaYz1LReLQIcZ0d70vB2eP2p+vPjyD4Pz7YeS9cHiVMeYDP9oP9BTlGNP4VeTsCVe8Dz2mNPpwG42DIwy6waietfF9WPuqMTVXXRTnQco+Y6kJz3bGdF8Drm/c4F51gqJg+mcQv8mYHvDIusr7HP0dPrzYqB427qnqqx9ZrfDzo0C5VLGThxEGExGRGmuzYank5GQ2btzIxo0b2bRpE5s2beLkSWPO0htvvJF58+Y1+DmtVisLFizgyy+/ZPPmzZw4cQI3NzdCQkIYPHgw48aN44YbbsDBQX/MVakGYaluwbZhqcOpORSVWDQfvYiIiIiIiIiIiEgTSEjP5ZFvd7Euxk51lAZiMsG4HsHMHBXByK4BmDTtkEjDsVqNyj5nAlAVg1HZSTXsp6TsWFZVbnfzKwtR2UyJFwHeHcBc7v2yrCTY9ils/RgyjlZ/Xmcv6He1Mb1d+341G2tjMpuh60XGUpvn4d8VrvnfuTNtmJMbjLrXqO4V/QucjLF9fWWfaLhzufjA+ffB8NuNKfJagrChcNNiowrW8mcgaXflfQ4sgYM/wYDrjGpiPh0r77NvUeXA1ej7wbt9owxbRORc1WbDUiEhIU16vqNHjzJjxgzWrbP9zys/P5/09HT279/P559/zuWXX46vr2+Tjq1VqRiWykmBgmxwKQtIdQuyDUsVlVg5cjK3UohKRERERERERERERBqO1Wrly03x/GPJPrILqpgKqZYczSZ83JzwcXPC+/S/Pdp7cc3QTkQENnGVGIHiAjjwE6QehKhLoH3/5h5R4ynKP13pxwqRlxgVks5FOalweDUkbLYNrhTnNf6589KN5fjWym1mJ/DtZISozI5GwKSqKdbOaD/AqCLV5yqb941aFK8QuOBBOP//jApZWz4yfqYqVsjqNgGu/ADcfJtlmI3K1cf+tGiFuUZlMnsBvfQjUFJw9r4dXGD4LOPr6+7fwANvACYTRE6AruNg19fw6/OQWSE0Z7XAts9g1zcwbJZRnezMcynKN6ZsLM+nE4yY3TTjFxE5h7TZsFR5nTp1okePHixbtqxR+o+Pj2fMmDEcPnwYBwcHrr/+eqZOnUp4eDgWi4XDhw+zfPlyvvvuu0Y5/znFt1PlbRlHIaRX6aqfhzOBni6kZpf90hSTnKWwlIiIiIiIiIiIiEgjOZ6RxyMLdrHmYIrddj93I+jk4+5cGn7ycXPEx80JXzdnmzCUj5sTvqf3d3d2UNWoluBkrBHq2P4/yDVm6WDNKzDzJ+g4uHnH1hgKsuCjyXBip7Fu+psRXhkyEyIvtq141NoU5RlTfh1aBbEry55jS2MpgrRYY6mOk4cRvBkyEzoMbJqxNQSzGSLHG8up46fDMV9DXkZZ2Kc1v87qwtkdgnsaS0UWC2QlVg5RpR02Hju6QrdxcOHD9qsxtTRmM/SfDr0vg80fwuqXIC/Ndp/ifPhtjlGF7Pz7jSpZG941AmXlXfx3o2qXiIjUSpsNSz311FMMHTqUoUOHEhISQlxcHBEREQ1+HqvVyvXXX8/hw4fx8/Pjxx9/5LzzzrPZZ9iwYUyfPp13331XU/CdjZMbeLU3fiE6Iz3OJiwFEBnsaROWik7KZmKfJhqjiIiIiIiIiIiISBthtVr5enMCzy3eS5adalLtfVz515X9uCAqqBlGJ/VSXAj7FxshqcNrKreXFMCPD8Ctvxpv/J8rLBZYMMs2RGS1QPRSY/HuCINugEF/MaaKa+ksFuO5HFppBKSO/F6zCj014dmubOq8itPomR0rVAY6bFQHSo+DzATAWr9zt+trTLPX92pw9a5fX83NuwNc+JCxiH1mM/iEGkvnUc09mobj6ALn3QEDZsBvb8Hvc6Eo13af/Exj2r4/3jOCnOV1Ggm9Lmuq0YqInFPabFjq2WefbZLzfP7556xZY/wR8f7771cKSpXn6Nhmvx2149e5cliqgsgQT34/dLJ0PTo5u/HHJSIiIiIiIiIiItKGnMjM55EFO1l1wH41qWlDOvLEpb3wdnVq4pFJvaQdhi3zjCnocux/b0sd3wY7v4IB1zbJ0JrEr8/BgR+rbj+VAKtegNX/gqiJRkWjrhe1rCpAGUeNqlGHVsKh1ZUr1tSUgwv4hRvhp4qhKN9woxJQdTwC7FceKy6AjPgKYaq4sunXinLs9+foBn2vhME3Q+ggY0ozkXOBqzdc9DgMvRXWvGRcgytOO1n+vVEATDDxn/o5EBGpI6VzGtncuXMB6N69O1ddZWf+Xak933CjROwZ9sJSFabci1FYSkRERERERERERKRBWK1Wvt16jGd/2ENWfuVqUiHeLrx4ZT/Gdg9uhtFJnZQUGQGhzR8ZAZvaWP4M9JwKLp5n3bXF2/k1rHutZvtaS+DAEmPx6QSDb4CBfwGvdo07RnvyMiBubVlAKu1Q7Y538jCq9bQfYBuK8mzXOFXDHF0gsJuxVGS1Qk6qbYgq9yQEdYc+V4KrT8OPR6Sl8AqBKa/CeXfCr8/DngVV7ztwBnQY0GRDExE51ygs1YiOHj3KH3/8AcDUqVNLtxcVFXHs2DEcHBxo164dTk76VE2t+HW2XbcTluoW7GWzHpuSTYnFioNZ6WoRERERERERERGRuko6lc9jC3axYn+y3fYrB3XkqUt74eOu+96tQvoR2PoxbPsMspOq39fNz5gqyj8CljxQtj37BKx7HcY92bhjbWwJW+D7u2y3mR1h+ufG12bLR0YlLXsyjxrBhlUvQvdJxvRwXcY2TtCopBhOHYO0WDjymxGQOr7VmCqwpkxmCB0MXcYY4+w4FBydG36sdWEygWeQsYQNa+7RiDSPgK5w9Ucw6h4jkHpolW27sydc9FRzjExE5JyhsFQjOhOUAujbty8nTpzg0UcfZf78+eTmGvPNuru7c/HFF/P0008zYMCAZhppK1ODsFRkiO0nWAqKLSSk5xIe4NF44xIRERERERERERE5R1mtVr7ffpynF+0hM6+oUnuQlwsvXtGXcT1DmmF0UislxRC91KgiFbMcsFa/f6eRxlRzPf8ETq5G5Z/9P0LsirJ9fnsLBt1gTNnWGmUegy+vhZIC2+2TX4buE43Hg2+E49uN0NTOr+1PFWcphn0/GItfZxh0Iwy8HjxrWWWtIMt2WrryVZYyjlaenqsm/LsYwaiuY6HzaHDzrX0fItK0OgyEG76H2F+N0FTiDiPsOPVNowqViIjUmcJSjWjv3r2lj9PS0ujXrx8pKbbze+fm5rJw4UKWLFnChx9+yPXXX1/r8yQkJFTbnphYcQ7bVq5iWCrjiPHHWbk5eQM8nPFzdyI9t+yP9uikbIWlRERERERERERERGopOSufx7/bzS977VceunxgKE9P7YWvewupTCP2ZSbA1k9g66eQdbz6fV19oP91MPgmCO5h22YywSUvwLsjjanowAgZLX8arp7XGCNvXIW58OV1lStrDZsFQ2623dZhAHR4Ey5+HnZ9bQTOTuy03296HKx4Fla+AD2mGIGzzhcY1aYsFshKLAtBpZULQ6UfNqadqy83f+hyoRGQ6jKm9QbZRAS6XmT8LKcdMqr8ufs394hERFo9haUaUVpaWunjRx99lPz8fK6//noefvhhoqKiSElJ4dNPP+Xpp5+msLCQm2++mZ49ezJ48OBanScsLKyhh96yVQxLFecbf8SUmwfcZDIRGezFxriy70F0cjbjeyllLSIiIiIiIiIiIlITVquVRTuMalIZuZWrSQV6OvOPy/tySe92do6WFuPoH7DuNYhedvap2sKGG1PI9b4MnNyq3i+4Bwy9FTa+V7Ztz3dGwCh8ZIMMu0lYrfD9nZC43XZ7lzFwyT+rPs7FywhSDZ4Jx7bClg9h9wIoyq28r6UI9i40Ft9wcHA2PgReUthwzwPAwQU6nWdUjuoyFtr1a5xpAEWkeZhMxvR8IiLSIBSWakQ5OWUlWPPz87n55pv573//W7otNDSURx55hPDwcK677jqKiop44okn+Omnn5pjuK2HZwg4uhohqTPS42zCUgDdQjwrhKWymmiAIiIiIiIiIiIiIq1banYBT3y3m5/3nLDbPrV/B579U2/8PVRNqsVK3gcr/g4Hfqx+Pxcf6D/dqCIV0rvm/Y95BHZ+BfkZZdt+fgRuW9V6QjprXjZCXuX5dzUqZDnU4C00kwk6DjaWS16AnfONalPJe+zvn3Gk3kMu5ewJgZHGlHpdx0KnEdUH3ERERESklMJSjcjV1bX0saOjIy+88ILd/a699lpee+01Nm/ezLJly8jIyMDX17fG54mPj6+2PTExkWHDhtW4vxbPbAbfTpB6sGxbepzxiYlyIoM9bdZjkrObYHAiIiIiIiIiIiIirduSnYk8+f1u0nIqV74J8HDm+cv6MKlv+2YYmdRIZgKs/Cfs+F/1laRCBxvVkXpfDs4etT+Puz+MfQx+eqhsW+IO2P45DPpL7ftranu/h5X/sN3m4gPXfmlMc1Vbrj4w7Daj4lbCJiM0tWeB7Qe/a8UE3h2M2Tb8Iox//U//69cZ3AOMsJaIiIiI1JrCUo3Iy8ur9PGAAQMICal6CrhLLrmEzZs3Y7FY2LJlC+PGjavxeTp27FivcbZKfp0rh6UqiAz2slmPSc7GYrFiNuuPBxEREREREREREZHysvKL+GnXCb7ZkmBTsb+8KX3b8/c/9ybA06WJRyc1kptmTLf3x/tQUmB/H2cv6He1MX1c+371P+eQm2HTfyH1QNm2FX83pvFz8arysGaXuAO+u912m8kMV38IQVH169tkgrBhxjLxBdjxFWz5CFL2V97X0a1CCKpcGMq3Ezi5Vj5GREREROpNYalGFBYWZvfx2fZNSUlptDGdM/w6267bC0uF2FaWyi0s4XhmHh393BtvXCIiIiIiIiIiIiKtRInFyvqYVL7dmsDSPSfIL7JfhcjP3YnnLuvDpf06NPEIpUYKc+GPf8O6N6Ag0/4+7gEw+kEYdAO4eNrfpy4cnIxA0GdXlm3LSYa1r8L4ZxruPA0pKwm+uA6Kcm23X/ICdBvfsOdy84Pzbofhf4X4jXBipxEiOxOM8gxWdSgRERGRZqCwVCPq3btsbu+SkpJq9y3f7uiob8tZ1SAsFezlgperI1n5xaXbopOzFZYSERERERERERGRNu1gUhbfbk1g4bZjJJ2qogLRaZf0DuH5y/oS5KVqUi1OSTFs/wxWvQhZifb3cfKAkbNhxGxw9W6ccXQbD5EXQ/Sysm2/vw2DbjQqJrUkRfnw1Qw4lWC7fdANMPx2+8c0BJMJOg03FhERERFpdkrlNKIhQ4bg5uZGXl4ehw4dqnbf2NjY0sehoaGNPbTWrwZhKZPJRGSwJ1uPZpRui0nKZmz34EYdmoiIiIiIiIiIiEhLczK7gEU7jrNg6zF2Haui+lA5gZ7OPHlpL/7UvwMmVb5pWaxW2PeDMd3dyWj7+5gdjan2LnzIqF7U2C55AWJ/BcvpDy+XFMIvT8L0zxr/3DVltcLi+yBhk+32TiNh8quq8CQiIiLShigs1Yg8PDyYOHEi3333HXv27CE6OprIyMhK+1ksFr7//nsA3N3dGTRoUFMPtfWpGJbKSjQ+EVJh/u7IYC+bsFR0clbjj01ERERERERERESkBSgoLmHl/mS+2XKMVQeSKbZYq93f0WxiTPcgrhzUkYt6BuPi6NBEI5UaO7wWlj8DxzZXvU+fK2Hs4xDQtcmGRWAkDJsFG94p27bvB2O8EaObbhzV+W0O7PjCdptvJ5j+KTg6N8+YRERERKRZKCxVD/PmzWPmzJkAPP300zzzzDOV9nnkkUf47rvvsFqt3HXXXSxZsgQnJyebfV544YXSylIzZ87ExUXljM/KN7zytoyjEBRlsykyxHbu9ejk7MYclYiIiIiIiIiIiEizslqtbI/PYMHWY/yw8zgZuUVnPaZPqDdXDOzInwZ0INCzhd6fLi6ErR/Drm+M6eQiLoAuYyGkd9uoCHRiFyx/FmJ+qXqfrhfBuKehw4AmG5aNCx+CHV9CXlrZtp8fhb+uBnMzB+8O/Ay/PG27zdkTrv0SPAKbZ0wiIiIi0mzabFhq3bp1xMTElK6npqaWPo6JiWHevHk2+9900011Os+wYcO48847eeedd/jll184//zzuf/++4mKiiIlJYXPPvuMzz4zytCGhYXZDVyJHS6e4B4IuWXfN9LjKoWlugXbhqVikrKxWq0qGy0iIiIiIiIiIiLnlOMZeXy37Rjfbk3gUErOWfcP9nLh8oGhXDGoI93beTXBCOvIYoE9C+DX54x7wGdELzP+9QiCLmOM4FSXMeAT2gyDbETpcbDyBdg5H6iiMlj7ATDhWeP5Nyc3Pxj7GPz4YNm2pF2w7VMYfFOzDYukvfDtLdh+/UxwxX+MsJ2IiIiItDltNiz1wQcf8PHHH9ttW79+PevXr7fZVtewFMCcOXPIzs7mk08+YePGjVx77bWV9unWrRuLFy8mMFCfYKgxv86Vw1IVRIbY/pGfVVBM0qkC2vm4VtpXREREREREREREpLVJySrgse92sXxfEtbqZ9nDxdHMJb3bceXgjozqGoCjg7lpBllXsb8a1YBO7Kx6n5wU2PW1sQAERhnBqa5jIXyUUYWqNcpOgbWvwqYPwFJFdTD/LnDRk9DrMjC3kO/l4Jmw+UNI3lu2bcVz0PtycPVp+vHknIQvroHCCrNOjHsKekxu+vGIiIiISIvQZsNSTcnBwYGPP/6Ya6+9lg8++IANGzaQkpKCp6cnvXv35sorr+Svf/0rrq4K8NSKX2fbednthKU6+Lji4exATmFJ6bbo5CyFpURERERERERERKTVyy8q4YYPN7Iv8VS1+w2L8OfKQaFM7tseL1enJhpdPRzbCsufgcOra39s6kFj2fgemB0hdIgRnOoyBkIHg0MLff7FBRC/EQ6tgkMr4fg2sFrs7+sRDGMegUE3tLzn4+AIl7wAn15Wti03Fda8Ahc/17RjKS6E+TdAxhHb7X2nwfn3N+1YRERERKRFabNhqXnz5lWaaq+2brrp/9m78/Co6rP/458z2UMC2UhYAklk32VVRGRRcWFR3BBFwa20Lo9aWqv2V6t2sbaKa7WtCyiKaEVwQdGCgIAgi+w7mIQAISEbCdmTOb8/IhPOzAQSyMxkeb+uK1fne5/7zLnTitfDPJ/5fqfVacepK6+8UldeeeU5PROniEy0rt2EpQzDUOfYMG05dNxR25dxQsO7tPbsbAAAAAAAAADgYX9etLPGoFRCdKiu6x+vif3bq2N0qJcnO0vZB6qO29uxoOae+MFSbM+qQFHewdO/n71CSltb9bP8GSkwXEoaXn1sX0wXyTDq9VeoNdOs2n3pwLKqgFTqaqm86PT3BIZLFz8oXXivFNjCK2OelU6jpG5XS3u+rK6tfb3qKL7oTt6ZwTSlr34rpa6y1tsPlCa84rv/3QEAANAgNNuwFJoA57CU87dDftbJOSyVecJtHwAAAAAAAAA0Fl9sPaL31lrDQuFB/hrXr62uHxCvgQmRMhpLIKTgqLTiWenHd6sCTu7EdJUu/aPUfWx10CXnp+qwUfIKqeS4+3tPKiuoCvCcDPG0bC8ljZBie1R93hyZKEUlSUHh9fSLOck/8vPOUT//nMio3X1+gdLge6ThM6QW0Z6Zrb6N+bO073/VRwjay6Vv/iBNnuud5697Q9o421oLbyfdPFcK4OQJAACA5o6wFBovdztLmabLN0K6xFr/Yrs/s8CzcwEAAAAAAACAB6VmF+rR+dssteAAm+bfe5G6xnko6OMJJcel1S9La1+reVel8HbSqMekfrdUHfF2qqjzqn4G3yXZK6Ujm6WfvpV+WiEdXFsd1KlJ/mFpi5vwTmi0FJlkDVCdfB3eTrLZavf7lRZIKaurdsH6abl0bHft7jvJP1jqfb004ndSZELd7vW16E7SBdOlNa9W1/Ysqvrv4byRnn32gW+lxY9aa/4hVUGt8DaefTYAAAAaBcJSaLyc/3JYdkIqypZaxFjKXWLDLOu9GSdkmmbj+VYVAAAAAAAAAPystKJS9839USdKrTswPX1N78YTlKoolda/KX33nFSc474nuJV08a+rAjcBIWd+T5ufFD+w6ueS30plhVLq99U7T2XuqP18RdlVP4c3uF7zC5QiEpxCVD//Z6v4qkDUgWVVAalD62veKcstQ2rbt+qIwE6jpA4XNu5dkEY8Im2ZJxVlVdcWPyZNX+kafKsvWful/06TzEprfeLrUrv+nnkmAAAAGh3CUmi8WraXbP7Wv2zmpriGpeKsYanjxeXKOlGm1uFBXhgSAAAAAACgSmpqql5++WUtWrRIaWlpCgoKUqdOnXTTTTfpvvvuU2ho6Dk/IyUlRa+//rqWLFmiAwcOqLCwUOHh4erevbuuvPJK/fKXv1RsbGw9/DYAfOWZL3dr++F8S+26/u1148B4H01UB/ZKaetH0rK/SMfT3Pf4B1cFpC5+WAqJPPtnBbaQulxe9SNJBRk/H3/3c3iqIP3s3reyTMreV/VTH1p1lDqNrApIJY1oPMfs1UZwK2n076UvHq6uZe6Ufnynajew+nb4R2n+3a7HMY54VOo1sf6fBwAAgEaLsBQaL5ufFNGx6lz6k3JTpPhBlrb4yFAF+dtUWmF31PZlFhCWAgAAAAAAXvP5559rypQpys+vDjgUFRVpw4YN2rBhg958800tWrRInTt3PutnzJkzR9OnT1dxcbGlnpubqzVr1mjNmjV66aWXNG/ePF1++eVn/RwAvrN4+1HN/j7FUjuvdQv96dreDXsnfdOU9n0jLXmyKizjjmGT+k+pCra0al//M4THSf0mVf2YpnRsT1Vo6ujWqs+Vc1OqjuXztOBWUtIlVUfRnTeq6hjBhvy/3bkaMFVa/5aUsb26tuwvVccLhkTUzzOyD0hLn5Z2LnS91vOaqmMMAQAAgFMQlkLjFpnoFJZKdmnxsxnq1DpMO9OrP4zcn3lCF3WKcekFAAAAAACob5s2bdKkSZNUXFyssLAwPfbYYxo1apSKi4s1b948vfHGG9q7d6/Gjh2rDRs2KDy87sdorV69WtOmTZPdbpfNZtPUqVN1zTXXqF27djp48KDeeecdff7558rJydE111yj7du367zzzvPAbwvAU9JyivTIx1sstSB/m/55ywC1CGrAH/WnrZf+94R08Puae7qPky59QmrdzTszGYYU273q51TlJVLewZ/DU8nVIaqcn19XFLu+15nYAqQOF1SFozqNktqe77kj6Boim5905TPSO+Ora0XZ0oq/S1f+9dzeu+CotOJZaeM7rsfuSVKbvtK1r0s227k9BwAAAE1OM/q/yNEkRSZa17kpbtu6xFnDUvsyTnhuJgAAAAAAgFM8+OCDKi4ulr+/v7755hsNHTrUcW306NHq0qWLHnnkEe3du1fPP/+8nnzyyTo/45lnnpHdXrWr9iuvvKJ7773XcW3w4MG6/vrrNWPGDM2cOVPFxcWaOXOmXn311XP+3QB4R1mFXfd/sEn5JRWW+h/H91KPti19NFUtrH6pKihVk44XSZc/JXUY4r2ZTicgWGrdterHmWlKJzJcA1Qng1UnMqp7Y3tW7Rp13kgp4SIpKMwr4zdYSZdUBeJ2f1FdW/dvadCdUsxZ7KhYclxa/bK09jWpvMh9T3QXafIHVccxAgAAAE4IS6FxcwlLpbpt6xJr/cvovswCDw0EAAAAAABQbd26dVq5cqUk6a677rIEpU6aMWOGZs2apV27dumll17S73//ewUEBNTpOd9/X7VjS3R0tCUodaonnnhCM2fOlCStWbOmTu8PwLf+8fVubUnLs9TG92unyUM6+GagMzHNqmPRVs10fz22l3TZH6UuYxrPEXSGIYW3qfrpeKHr9bJCKf+IFBIpteBUAxdj/lR1FGNlWdXaXiF983vplg9r/x7lJdL6N6WVz0vFOe57giOk4TOkIfdIASHnPDYAAACaJvYeReNWy7BU51jr9vX7M9lZCgAAAAAAeN7ChQsdr++44w63PTabTbfffrskKS8vT8uWLavzc8rKqv6fz0lJSTX2tGrVSjExMZZ+AA3f0l0ZemNlsqWWGB2qv07sLaMhBo3sdmnRDPdBqVYdpGv/Jf1ypdT1isYTlKqNwBZSTBeCUjWJOk+68FfW2t7F0v6lZ77XXiltniu9OqgqYOUuKOUfLF38sPTgFmnY/xGUAgAAwGkRlkLjFpFgXecfkipcP+zrEmfdWSrrRJlyCvlQEAAAAAAAeNaqVaskSS1atNDAgQNr7BsxYoTj9erVq+v8nG7dukmSkpOTa+zJz89XVlaWpR9Aw3Ykr1gz/rvFUgv0s+nVWwYoPLhuO9B5RWW5tGC6tOEt12sjH5fu3yCdP1my+Xl/Nvje8N9ILWKtta8flyor3PebprRnsfSvi6WFv5KOp7n2GDZpwFTp/zZJlz0phUTU99QAAABogghLoXFz3lnKtLv9C1NCVKgC/KzfUmJ3KQAAAAAA4Gm7du2SJHXu3Fn+/v419nXv3t3lnrr45S9/KUnKzs7Wv/71L7c9f/rTn1z6ATRc5ZV2PfDBJuUVlVvq/29cD/Vu38pHU51GeYn00e3Sto+sdcMmTXhVGvk7KSDYN7OhYQhuKV36B2vt2G5p4yzX3oM/SLOukj6YJGXudP9+PcZL9/4gTXhZatmu/ucFAABAk1XzJzRAYxASUXUGeUledS03RYruZGnz97PpvJgw7ckocNT2ZRZoSFKUN6YEAAAAAADNUElJiWMnp/j4+NP2RkZGqkWLFiosLFRampudM87gzjvv1KpVq/Tuu+/qvvvu08aNGzVhwgS1bdtWBw8e1Jw5cxxHAv7+97/XZZddVudnHDp06LTX09PT6/yeAGr2/Dd7tTE111K7uk8b3XZhQg13+FDpCWneZCn5O2vdFiBd/4bUa6Jv5kLDc/6t0rr/SEe3VdeW/UXqfb0UGiVl7paWPi3tWVTzeyRcXLWLVIfBHh8XAAAATRNhKTR+kYlS+ubqdW6K27bOcdawFDtLAQAAAAAATyooqP4cIiws7Iz9J8NSJ07U/TMLPz8/vfPOOxo/frz++te/6s0339Sbb75p6Rk1apQef/zxswpKSVKHDh3O6j4Adbd8T6b+teKApdYhKkTPXNdXhmHUcJePFOVI798oHd5grfuHSJPek7qc3b9z0ETZ/KQrn5VmX11dK86Vvv69ZLNJm+dWnSDhTlzvqpBU58ukhvbnAAAAAI0Kx/Ch8XM+iq+GsFSXWOuHkoSlAAAAAACAJ5WUlDheBwYGnrE/KChIklRcXHxWz9u1a5feffddbdu2ze31NWvW6K233tLhw4fP6v0BeMfR4yX69UdbLLUAP0OvTh6gViEBPpqqBgUZ0uxxrkGpoJbSbZ8QlIJ7icOkntdYa1vmSpvecx+UatVRmvgfafpKqcvlBKUAAABwzghLofFzDkvlpbpt6xIbblnvyyAsBQAAAAAAPCc4ONjxuqys7Iz9paWlkqSQkJA6P2vlypUaOnSoPv/8c7Vv315z5szR0aNHVVZWprS0NP3zn/9UaGio5s2bpyFDhmjHjh11fkZaWtppf9atW1fn9wRgVVFp1//N26ScQuu/Mx69qof6dYjwzVA1yTsozbpSynT690lotDT1cynhIt/Mhcbh8qclv6DT94RGS1f+TXpgg9RvUtXOUwAAAEA94Bg+NH613Vkqzrqz1NH8EuWXlKtlcAP7NhYAAAAAAGgSwsOrv7hVm6P1CgsLJdXuyL5TlZaWavLkyTp+/LjatGmjtWvXqk2bNo7r8fHxuvfeezVixAgNGjRIR44c0dSpU7Vhw4bTvKur+Pj4OvUDqLuXlu7TuuQcS+3ynnG6c1iibwaqybG90pxrpXynnerC20m3L5Rad/PFVGhMIhOlofdJq2a6XgtoUXXtogek4JZeHw0AAAB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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 390,
       "width": 1189
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 4))\n",
    "\n",
    "# 绘制第一个子图\n",
    "axs[0].plot(train_ls, label=\"train loss\")\n",
    "\n",
    "axs[0].plot(test_ls, label=\"test loss\")\n",
    "axs[0].set_xlabel(\"Epoch\")\n",
    "axs[0].set_ylabel(\"Loss\")\n",
    "axs[0].legend()\n",
    "\n",
    "# 绘制第二个子图\n",
    "axs[1].plot(train_acc_ls, label=\"train_acc\")\n",
    "axs[1].plot(test_acc_ls, label=\"test_acc\")\n",
    "axs[1].axhline(y=0.9, color='r', linestyle='--')  # 添加 y=0.9 的参考线\n",
    "axs[1].axhline(y=0.85, color='b', linestyle='--')  # 添加 y=0.85 的参考线\n",
    "axs[1].set_xlabel(\"Epoch\")\n",
    "axs[1].set_ylabel(\"Accuracy\")\n",
    "axs[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test accuaracy:  0.865\n",
      "Best train accuaracy:  0.91\n"
     ]
    }
   ],
   "source": [
    "print(\"Best test accuaracy: \", max(test_acc_ls))\n",
    "print(\"Best train accuaracy: \", max(train_acc_ls))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
